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Sample records for 3d face recognition

  1. Random-profiles-based 3D face recognition system.

    PubMed

    Kim, Joongrock; Yu, Sunjin; Lee, Sangyoun

    2014-03-31

    In this paper, a noble nonintrusive three-dimensional (3D) face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D) face recognition systems can achieve a reliable recognition rate under certain conditions, their performance is limited by internal and external changes, such as illumination and pose variation. To address these issues, 3D face recognition, which uses 3D face data, has recently received much attention. However, the performance of 3D face recognition highly depends on the precision of acquired 3D face data, while also requiring more computational power and storage capacity than 2D face recognition systems. In this paper, we present a developed nonintrusive 3D face modeling system composed of a stereo vision system and an invisible near-infrared line laser, which can be directly applied to profile-based 3D face recognition. We further propose a novel random-profile-based 3D face recognition method that is memory-efficient and pose-invariant. The experimental results demonstrate that the reconstructed 3D face data consists of more than 50 k 3D point clouds and a reliable recognition rate against pose variation.

  2. Random-Profiles-Based 3D Face Recognition System

    PubMed Central

    Joongrock, Kim; Sunjin, Yu; Sangyoun, Lee

    2014-01-01

    In this paper, a noble nonintrusive three-dimensional (3D) face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D) face recognition systems can achieve a reliable recognition rate under certain conditions, their performance is limited by internal and external changes, such as illumination and pose variation. To address these issues, 3D face recognition, which uses 3D face data, has recently received much attention. However, the performance of 3D face recognition highly depends on the precision of acquired 3D face data, while also requiring more computational power and storage capacity than 2D face recognition systems. In this paper, we present a developed nonintrusive 3D face modeling system composed of a stereo vision system and an invisible near-infrared line laser, which can be directly applied to profile-based 3D face recognition. We further propose a novel random-profile-based 3D face recognition method that is memory-efficient and pose-invariant. The experimental results demonstrate that the reconstructed 3D face data consists of more than 50 k 3D point clouds and a reliable recognition rate against pose variation. PMID:24691101

  3. 3D face database for human pattern recognition

    NASA Astrophysics Data System (ADS)

    Song, LiMei; Lu, Lu

    2008-10-01

    Face recognition is an essential work to ensure human safety. It is also an important task in biomedical engineering. 2D image is not enough for precision face recognition. 3D face data includes more exact information, such as the precision size of eyes, mouth, etc. 3D face database is an important part in human pattern recognition. There is a lot of method to get 3D data, such as 3D laser scan system, 3D phase measurement, shape from shading, shape from motion, etc. This paper will introduce a non-orbit, non-contact, non-laser 3D measurement system. The main idea is from shape from stereo technique. Two cameras are used in different angle. A sequence of light will project on the face. Human face, human head, human tooth, human body can all be measured by the system. The visualization data of each person can form to a large 3D face database, which can be used in human recognition. The 3D data can provide a vivid copy of a face, so the recognition exactness can be reached to 100%. Although the 3D data is larger than 2D image, it can be used in the occasion where only few people include, such as the recognition of a family, a small company, etc.

  4. Appearance-based color face recognition with 3D model

    NASA Astrophysics Data System (ADS)

    Wang, Chengzhang; Bai, Xiaoming

    2013-03-01

    Appearance-based face recognition approaches explore color cues of face images, i.e. grey or color information for recognition task. They first encode color face images, and then extract facial features for classification. Similar to conventional singular value decomposition, hypercomplex matrix also exists singular value decomposition on hypercomplex field. In this paper, a novel color face recognition approach based on hypercomplex singular value decomposition is proposed. The approach employs hypercomplex to encode color face information of different channels simultaneously. Hypercomplex singular value decomposition is utilized then to compute the basis vectors of the color face subspace. To improve learning efficiency of the algorithm, 3D active deformable model is exploited to generate virtual face images. Color face samples are projected onto the subspace and projection coefficients are utilized as facial features. Experimental results on CMU PIE face database verify the effectiveness of the proposed approach.

  5. 3D face detection and face recognition: state of the art and trends

    NASA Astrophysics Data System (ADS)

    Li, Xilai; Li, Aihua; Bai, Xiangfeng

    2010-08-01

    Face detection and recognition are old but rapidly growing research areas due to increasing demands for security in industrial and law enforcement applications. Face recognition based on two-dimensional (2D) images have reached a significant level of maturity with some practical success. However the performance of 2D face recognition may degrade under poor illumination conditions, pose changing or for subjects of various skin colors. 3D face recognition represents a viable alternative to 2D face recognition in the research for a robust and practical identification system. This paper provides a review of research achievement in face recognition from 2007 to present. This survey mainly include three parts: (1) advances in face detections, (2) state of the art of 3D face recognition and (3) face images database and face recognition algorithm evaluation. Recent research has also demonstrated that the fusion of 2D and 3D face recognition can improve the overall performance of face recognition algorithm.

  6. 3D fast wavelet network model-assisted 3D face recognition

    NASA Astrophysics Data System (ADS)

    Said, Salwa; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

    2015-12-01

    In last years, the emergence of 3D shape in face recognition is due to its robustness to pose and illumination changes. These attractive benefits are not all the challenges to achieve satisfactory recognition rate. Other challenges such as facial expressions and computing time of matching algorithms remain to be explored. In this context, we propose our 3D face recognition approach using 3D wavelet networks. Our approach contains two stages: learning stage and recognition stage. For the training we propose a novel algorithm based on 3D fast wavelet transform. From 3D coordinates of the face (x,y,z), we proceed to voxelization to get a 3D volume which will be decomposed by 3D fast wavelet transform and modeled after that with a wavelet network, then their associated weights are considered as vector features to represent each training face . For the recognition stage, an unknown identity face is projected on all the training WN to obtain a new vector features after every projection. A similarity score is computed between the old and the obtained vector features. To show the efficiency of our approach, experimental results were performed on all the FRGC v.2 benchmark.

  7. 3D face recognition by projection-based methods

    NASA Astrophysics Data System (ADS)

    Dutagaci, Helin; Sankur, Bülent; Yemez, Yücel

    2006-02-01

    In this paper, we investigate recognition performances of various projection-based features applied on registered 3D scans of faces. Some features are data driven, such as ICA-based features or NNMF-based features. Other features are obtained using DFT or DCT-based schemes. We apply the feature extraction techniques to three different representations of registered faces, namely, 3D point clouds, 2D depth images and 3D voxel. We consider both global and local features. Global features are extracted from the whole face data, whereas local features are computed over the blocks partitioned from 2D depth images. The block-based local features are fused both at feature level and at decision level. The resulting feature vectors are matched using Linear Discriminant Analysis. Experiments using different combinations of representation types and feature vectors are conducted on the 3D-RMA dataset.

  8. Robust 3D face recognition by local shape difference boosting.

    PubMed

    Wang, Yueming; Liu, Jianzhuang; Tang, Xiaoou

    2010-10-01

    This paper proposes a new 3D face recognition approach, Collective Shape Difference Classifier (CSDC), to meet practical application requirements, i.e., high recognition performance, high computational efficiency, and easy implementation. We first present a fast posture alignment method which is self-dependent and avoids the registration between an input face against every face in the gallery. Then, a Signed Shape Difference Map (SSDM) is computed between two aligned 3D faces as a mediate representation for the shape comparison. Based on the SSDMs, three kinds of features are used to encode both the local similarity and the change characteristics between facial shapes. The most discriminative local features are selected optimally by boosting and trained as weak classifiers for assembling three collective strong classifiers, namely, CSDCs with respect to the three kinds of features. Different schemes are designed for verification and identification to pursue high performance in both recognition and computation. The experiments, carried out on FRGC v2 with the standard protocol, yield three verification rates all better than 97.9 percent with the FAR of 0.1 percent and rank-1 recognition rates above 98 percent. Each recognition against a gallery with 1,000 faces only takes about 3.6 seconds. These experimental results demonstrate that our algorithm is not only effective but also time efficient.

  9. IR Fringe Projection for 3D Face Recognition

    NASA Astrophysics Data System (ADS)

    Spagnolo, Giuseppe Schirripa; Cozzella, Lorenzo; Simonetti, Carla

    2010-04-01

    Facial recognitions of people can be used for the identification of individuals, or can serve as verification e.g. for access controls. The process requires that the facial data is captured and then compared with stored reference data. Different from traditional methods which use 2D images to recognize human faces, this article shows a known shape extraction methodology applied to the extraction of 3D human faces conjugated with a non conventional optical system able to work in ``invisible'' way. The proposed method is experimentally simple, and it has a low-cost set-up.

  10. 3D multi-spectrum sensor system with face recognition.

    PubMed

    Kim, Joongrock; Yu, Sunjin; Kim, Ig-Jae; Lee, Sangyoun

    2013-09-25

    This paper presents a novel three-dimensional (3D) multi-spectrum sensor system, which combines a 3D depth sensor and multiple optical sensors for different wavelengths. Various image sensors, such as visible, infrared (IR) and 3D sensors, have been introduced into the commercial market. Since each sensor has its own advantages under various environmental conditions, the performance of an application depends highly on selecting the correct sensor or combination of sensors. In this paper, a sensor system, which we will refer to as a 3D multi-spectrum sensor system, which comprises three types of sensors, visible, thermal-IR and time-of-flight (ToF), is proposed. Since the proposed system integrates information from each sensor into one calibrated framework, the optimal sensor combination for an application can be easily selected, taking into account all combinations of sensors information. To demonstrate the effectiveness of the proposed system, a face recognition system with light and pose variation is designed. With the proposed sensor system, the optimal sensor combination, which provides new effectively fused features for a face recognition system, is obtained.

  11. 3D Multi-Spectrum Sensor System with Face Recognition

    PubMed Central

    Kim, Joongrock; Yu, Sunjin; Kim, Ig-Jae; Lee, Sangyoun

    2013-01-01

    This paper presents a novel three-dimensional (3D) multi-spectrum sensor system, which combines a 3D depth sensor and multiple optical sensors for different wavelengths. Various image sensors, such as visible, infrared (IR) and 3D sensors, have been introduced into the commercial market. Since each sensor has its own advantages under various environmental conditions, the performance of an application depends highly on selecting the correct sensor or combination of sensors. In this paper, a sensor system, which we will refer to as a 3D multi-spectrum sensor system, which comprises three types of sensors, visible, thermal-IR and time-of-flight (ToF), is proposed. Since the proposed system integrates information from each sensor into one calibrated framework, the optimal sensor combination for an application can be easily selected, taking into account all combinations of sensors information. To demonstrate the effectiveness of the proposed system, a face recognition system with light and pose variation is designed. With the proposed sensor system, the optimal sensor combination, which provides new effectively fused features for a face recognition system, is obtained. PMID:24072025

  12. Pose invariant face recognition: 3D model from single photo

    NASA Astrophysics Data System (ADS)

    Napoléon, Thibault; Alfalou, Ayman

    2017-02-01

    Face recognition is widely studied in the literature for its possibilities in surveillance and security. In this paper, we report a novel algorithm for the identification task. This technique is based on an optimized 3D modeling allowing to reconstruct faces in different poses from a limited number of references (i.e. one image by class/person). Particularly, we propose to use an active shape model to detect a set of keypoints on the face necessary to deform our synthetic model with our optimized finite element method. Indeed, in order to improve our deformation, we propose a regularization by distances on graph. To perform the identification we use the VanderLugt correlator well know to effectively address this task. On the other hand we add a difference of Gaussian filtering step to highlight the edges and a description step based on the local binary patterns. The experiments are performed on the PHPID database enhanced with our 3D reconstructed faces of each person with an azimuth and an elevation ranging from -30° to +30°. The obtained results prove the robustness of our new method with 88.76% of good identification when the classic 2D approach (based on the VLC) obtains just 44.97%.

  13. Face recognition based on matching of local features on 3D dynamic range sequences

    NASA Astrophysics Data System (ADS)

    Echeagaray-Patrón, B. A.; Kober, Vitaly

    2016-09-01

    3D face recognition has attracted attention in the last decade due to improvement of technology of 3D image acquisition and its wide range of applications such as access control, surveillance, human-computer interaction and biometric identification systems. Most research on 3D face recognition has focused on analysis of 3D still data. In this work, a new method for face recognition using dynamic 3D range sequences is proposed. Experimental results are presented and discussed using 3D sequences in the presence of pose variation. The performance of the proposed method is compared with that of conventional face recognition algorithms based on descriptors.

  14. The Role of Active Exploration of 3D Face Stimuli on Recognition Memory of Facial Information

    ERIC Educational Resources Information Center

    Liu, Chang Hong; Ward, James; Markall, Helena

    2007-01-01

    Research on face recognition has mainly relied on methods in which observers are relatively passive viewers of face stimuli. This study investigated whether active exploration of three-dimensional (3D) face stimuli could facilitate recognition memory. A standard recognition task and a sequential matching task were employed in a yoked design.…

  15. The Role of Active Exploration of 3D Face Stimuli on Recognition Memory of Facial Information

    ERIC Educational Resources Information Center

    Liu, Chang Hong; Ward, James; Markall, Helena

    2007-01-01

    Research on face recognition has mainly relied on methods in which observers are relatively passive viewers of face stimuli. This study investigated whether active exploration of three-dimensional (3D) face stimuli could facilitate recognition memory. A standard recognition task and a sequential matching task were employed in a yoked design.…

  16. A framework for the recognition of 3D faces and expressions

    NASA Astrophysics Data System (ADS)

    Li, Chao; Barreto, Armando

    2006-04-01

    Face recognition technology has been a focus both in academia and industry for the last couple of years because of its wide potential applications and its importance to meet the security needs of today's world. Most of the systems developed are based on 2D face recognition technology, which uses pictures for data processing. With the development of 3D imaging technology, 3D face recognition emerges as an alternative to overcome the difficulties inherent with 2D face recognition, i.e. sensitivity to illumination conditions and orientation positioning of the subject. But 3D face recognition still needs to tackle the problem of deformation of facial geometry that results from the expression changes of a subject. To deal with this issue, a 3D face recognition framework is proposed in this paper. It is composed of three subsystems: an expression recognition system, a system for the identification of faces with expression, and neutral face recognition system. A system for the recognition of faces with one type of expression (happiness) and neutral faces was implemented and tested on a database of 30 subjects. The results proved the feasibility of this framework.

  17. 3D face recognition under expressions, occlusions, and pose variations.

    PubMed

    Drira, Hassen; Ben Amor, Boulbaba; Srivastava, Anuj; Daoudi, Mohamed; Slama, Rim

    2013-09-01

    We propose a novel geometric framework for analyzing 3D faces, with the specific goals of comparing, matching, and averaging their shapes. Here we represent facial surfaces by radial curves emanating from the nose tips and use elastic shape analysis of these curves to develop a Riemannian framework for analyzing shapes of full facial surfaces. This representation, along with the elastic Riemannian metric, seems natural for measuring facial deformations and is robust to challenges such as large facial expressions (especially those with open mouths), large pose variations, missing parts, and partial occlusions due to glasses, hair, and so on. This framework is shown to be promising from both--empirical and theoretical--perspectives. In terms of the empirical evaluation, our results match or improve upon the state-of-the-art methods on three prominent databases: FRGCv2, GavabDB, and Bosphorus, each posing a different type of challenge. From a theoretical perspective, this framework allows for formal statistical inferences, such as the estimation of missing facial parts using PCA on tangent spaces and computing average shapes.

  18. Description and recognition of faces from 3D data

    NASA Astrophysics Data System (ADS)

    Coombes, Anne M.; Richards, Robin; Linney, Alfred D.; Bruce, Vicki; Fright, Rick

    1992-12-01

    A method based on differential geometry, is presented for mathematically describing the shape of the facial surface. Three-dimensional data for the face are collected by optical surface scanning. The method allows the segmentation of the face into regions of a particular `surface type,' according to the surface curvature. Eight different surface types are produced which all have perceptually meaningful interpretations. The correspondence of the surface type regions to the facial features are easily visualized, allowing a qualitative assessment of the face. A quantitative description of the face in terms of the surface type regions can be produced and the variation of the description between faces is demonstrated. A set of optical surface scans can be registered together and averages to produce an average male and average female face. Thus an assessment of how individuals vary from the average can be made as well as a general statement about the differences between male and female faces. This method will enable an investigation to be made as to how reliably faces can be individuated by their surface shape which, if feasible, may be the basis of an automatic system for recognizing faces. It also has applications in physical anthropology, for classification of the face, facial reconstructive surgery, to quantify the changes in a face altered by reconstructive surgery and growth, and in visual perception, to assess the recognizability of faces. Examples of some of these applications are presented.

  19. Use of 3D faces facilitates facial expression recognition in children

    PubMed Central

    Wang, Lamei; Chen, Wenfeng; Li, Hong

    2017-01-01

    This study assessed whether presenting 3D face stimuli could facilitate children’s facial expression recognition. Seventy-one children aged between 3 and 6 participated in the study. Their task was to judge whether a face presented in each trial showed a happy or fearful expression. Half of the face stimuli were shown with 3D representations, whereas the other half of the images were shown as 2D pictures. We compared expression recognition under these conditions. The results showed that the use of 3D faces improved the speed of facial expression recognition in both boys and girls. Moreover, 3D faces improved boys’ recognition accuracy for fearful expressions. Since fear is the most difficult facial expression for children to recognize, the facilitation effect of 3D faces has important practical implications for children with difficulties in facial expression recognition. The potential benefits of 3D representation for other expressions also have implications for developing more realistic assessments of children’s expression recognition. PMID:28368008

  20. 3D Face Recognition Based on Multiple Keypoint Descriptors and Sparse Representation

    PubMed Central

    Zhang, Lin; Ding, Zhixuan; Li, Hongyu; Shen, Ying; Lu, Jianwei

    2014-01-01

    Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. To be useful in real-world applications, a 3D face recognition approach should be able to handle these challenges. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD) and the sparse representation-based classification (SRC). We call the proposed method 3DMKDSRC for short. Specifically, with 3DMKDSRC, each 3D face scan is represented as a set of descriptor vectors extracted from keypoints by meshSIFT. Descriptor vectors of gallery samples form the gallery dictionary. Given a probe 3D face scan, its descriptors are extracted at first and then its identity can be determined by using a multitask SRC. The proposed 3DMKDSRC approach does not require the pre-alignment between two face scans and is quite robust to the problems of missing data, occlusions and expressions. Its superiority over the other leading 3D face recognition schemes has been corroborated by extensive experiments conducted on three benchmark databases, Bosphorus, GavabDB, and FRGC2.0. The Matlab source code for 3DMKDSRC and the related evaluation results are publicly available at http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm. PMID:24940876

  1. 3D face recognition based on multiple keypoint descriptors and sparse representation.

    PubMed

    Zhang, Lin; Ding, Zhixuan; Li, Hongyu; Shen, Ying; Lu, Jianwei

    2014-01-01

    Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. To be useful in real-world applications, a 3D face recognition approach should be able to handle these challenges. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD) and the sparse representation-based classification (SRC). We call the proposed method 3DMKDSRC for short. Specifically, with 3DMKDSRC, each 3D face scan is represented as a set of descriptor vectors extracted from keypoints by meshSIFT. Descriptor vectors of gallery samples form the gallery dictionary. Given a probe 3D face scan, its descriptors are extracted at first and then its identity can be determined by using a multitask SRC. The proposed 3DMKDSRC approach does not require the pre-alignment between two face scans and is quite robust to the problems of missing data, occlusions and expressions. Its superiority over the other leading 3D face recognition schemes has been corroborated by extensive experiments conducted on three benchmark databases, Bosphorus, GavabDB, and FRGC2.0. The Matlab source code for 3DMKDSRC and the related evaluation results are publicly available at http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm.

  2. The impact of specular highlights on 3D-2D face recognition

    NASA Astrophysics Data System (ADS)

    Christlein, Vincent; Riess, Christian; Angelopoulou, Elli; Evangelopoulos, Georgios; Kakadiaris, Ioannis

    2013-05-01

    One of the most popular form of biometrics is face recognition. Face recognition techniques typically assume that a face exhibits Lambertian reectance. However, a face often exhibits prominent specularities, especially in outdoor environments. These specular highlights can compromise an identity authentication. In this work, we analyze the impact of such highlights on a 3D-2D face recognition system. First, we investigate three different specularity removal methods as preprocessing steps for face recognition. Then, we explicitly model facial specularities within the face detection system with the Cook-Torrance reflectance model. In our experiments, specularity removal increases the recognition rate on an outdoor face database by about 5% at a false alarm rate of 10-3. The integration of the Cook-Torrance model further improves these results, increasing the verification rate by 19% at a FAR of 10-3.

  3. A 2D range Hausdorff approach for 3D face recognition.

    SciTech Connect

    Koch, Mark William; Russ, Trina Denise; Little, Charles Quentin

    2005-04-01

    This paper presents a 3D facial recognition algorithm based on the Hausdorff distance metric. The standard 3D formulation of the Hausdorff matching algorithm has been modified to operate on a 2D range image, enabling a reduction in computation from O(N2) to O(N) without large storage requirements. The Hausdorff distance is known for its robustness to data outliers and inconsistent data between two data sets, making it a suitable choice for dealing with the inherent problems in many 3D datasets due to sensor noise and object self-occlusion. For optimal performance, the algorithm assumes a good initial alignment between probe and template datasets. However, to minimize the error between two faces, the alignment can be iteratively refined. Results from the algorithm are presented using 3D face images from the Face Recognition Grand Challenge database version 1.0.

  4. Face recognition using 3D facial shape and color map information: comparison and combination

    NASA Astrophysics Data System (ADS)

    Godil, Afzal; Ressler, Sandy; Grother, Patrick

    2004-08-01

    In this paper, we investigate the use of 3D surface geometry for face recognition and compare it to one based on color map information. The 3D surface and color map data are from the CAESAR anthropometric database. We find that the recognition performance is not very different between 3D surface and color map information using a principal component analysis algorithm. We also discuss the different techniques for the combination of the 3D surface and color map information for multi-modal recognition by using different fusion approaches and show that there is significant improvement in results. The effectiveness of various techniques is compared and evaluated on a dataset with 200 subjects in two different positions.

  5. 3D face recognition based on the hierarchical score-level fusion classifiers

    NASA Astrophysics Data System (ADS)

    Mráček, Štěpán.; Váša, Jan; Lankašová, Karolína; Drahanský, Martin; Doležel, Michal

    2014-05-01

    This paper describes the 3D face recognition algorithm that is based on the hierarchical score-level fusion clas-sifiers. In a simple (unimodal) biometric pipeline, the feature vector is extracted from the input data and subsequently compared with the template stored in the database. In our approachm, we utilize several feature extraction algorithms. We use 6 different image representations of the input 3D face data. Moreover, we are using Gabor and Gauss-Laguerre filter banks applied on the input image data that yield to 12 resulting feature vectors. Each representation is compared with corresponding counterpart from the biometric database. We also add the recognition based on the iso-geodesic curves. The final score-level fusion is performed on 13 comparison scores using the Support Vector Machine (SVM) classifier.

  6. A prescreener for 3D face recognition using radial symmerty and the Hausdorff fraction.

    SciTech Connect

    Koudelka, Melissa L.; Koch, Mark William; Russ, Trina Denise

    2005-04-01

    Face recognition systems require the ability to efficiently scan an existing database of faces to locate a match for a newly acquired face. The large number of faces in real world databases makes computationally intensive algorithms impractical for scanning entire databases. We propose the use of more efficient algorithms to 'prescreen' face databases, determining a limited set of likely matches that can be processed further to identify a match. We use both radial symmetry and shape to extract five features of interest on 3D range images of faces. These facial features determine a very small subset of discriminating points which serve as input to a prescreening algorithm based on a Hausdorff fraction. We show how to compute the Haudorff fraction in linear O(n) time using a range image representation. Our feature extraction and prescreening algorithms are verified using the FRGC v1.0 3D face scan data. Results show 97% of the extracted facial features are within 10 mm or less of manually marked ground truth, and the prescreener has a rank 6 recognition rate of 100%.

  7. 3D face recognition using simulated annealing and the surface interpenetration measure.

    PubMed

    Queirolo, Chauã C; Silva, Luciano; Bellon, Olga R P; Segundo, Maurício Pamplona

    2010-02-01

    This paper presents a novel automatic framework to perform 3D face recognition. The proposed method uses a Simulated Annealing-based approach (SA) for range image registration with the Surface Interpenetration Measure (SIM), as similarity measure, in order to match two face images. The authentication score is obtained by combining the SIM values corresponding to the matching of four different face regions: circular and elliptical areas around the nose, forehead, and the entire face region. Then, a modified SA approach is proposed taking advantage of invariant face regions to better handle facial expressions. Comprehensive experiments were performed on the FRGC v2 database, the largest available database of 3D face images composed of 4,007 images with different facial expressions. The experiments simulated both verification and identification systems and the results compared to those reported by state-of-the-art works. By using all of the images in the database, a verification rate of 96.5 percent was achieved at a False Acceptance Rate (FAR) of 0.1 percent. In the identification scenario, a rank-one accuracy of 98.4 percent was achieved. To the best of our knowledge, this is the highest rank-one score ever achieved for the FRGC v2 database when compared to results published in the literature.

  8. Template protection and its implementation in 3D face recognition systems

    NASA Astrophysics Data System (ADS)

    Zhou, Xuebing

    2007-04-01

    As biometric recognition systems are widely applied in various application areas, security and privacy risks have recently attracted the attention of the biometric community. Template protection techniques prevent stored reference data from revealing private biometric information and enhance the security of biometrics systems against attacks such as identity theft and cross matching. This paper concentrates on a template protection algorithm that merges methods from cryptography, error correction coding and biometrics. The key component of the algorithm is to convert biometric templates into binary vectors. It is shown that the binary vectors should be robust, uniformly distributed, statistically independent and collision-free so that authentication performance can be optimized and information leakage can be avoided. Depending on statistical character of the biometric template, different approaches for transforming biometric templates into compact binary vectors are presented. The proposed methods are integrated into a 3D face recognition system and tested on the 3D facial images of the FRGC database. It is shown that the resulting binary vectors provide an authentication performance that is similar to the original 3D face templates. A high security level is achieved with reasonable false acceptance and false rejection rates of the system, based on an efficient statistical analysis. The algorithm estimates the statistical character of biometric templates from a number of biometric samples in the enrollment database. For the FRGC 3D face database, the small distinction of robustness and discriminative power between the classification results under the assumption of uniquely distributed templates and the ones under the assumption of Gaussian distributed templates is shown in our tests.

  9. Using Facial Symmetry to Handle Pose Variations in Real-World 3D Face Recognition.

    PubMed

    Passalis, Georgios; Perakis, Panagiotis; Theoharis, Theoharis; Kakadiaris, Ioannis A

    2011-10-01

    The uncontrolled conditions of real-world biometric applications pose a great challenge to any face recognition approach. The unconstrained acquisition of data from uncooperative subjects may result in facial scans with significant pose variations along the yaw axis. Such pose variations can cause extensive occlusions, resulting in missing data. In this paper, a novel 3D face recognition method is proposed that uses facial symmetry to handle pose variations. It employs an automatic landmark detector that estimates pose and detects occluded areas for each facial scan. Subsequently, an Annotated Face Model is registered and fitted to the scan. During fitting, facial symmetry is used to overcome the challenges of missing data. The result is a pose invariant geometry image. Unlike existing methods that require frontal scans, the proposed method performs comparisons among interpose scans using a wavelet-based biometric signature. It is suitable for real-world applications as it only requires half of the face to be visible to the sensor. The proposed method was evaluated using databases from the University of Notre Dame and the University of Houston that, to the best of our knowledge, include the most challenging pose variations publicly available. The average rank-one recognition rate of the proposed method in these databases was 83.7 percent.

  10. Intraclass retrieval of nonrigid 3D objects: application to face recognition.

    PubMed

    Passalis, Georgios; Kakadiaris, Ioannis A; Theoharis, Theoharis

    2007-02-01

    As the size of the available collections of 3D objects grows, database transactions become essential for their management with the key operation being retrieval (query). Large collections are also precategorized into classes so that a single class contains objects of the same type (e.g., human faces, cars, four-legged animals). It is shown that general object retrieval methods are inadequate for intraclass retrieval tasks. We advocate that such intraclass problems require a specialized method that can exploit the basic class characteristics in order to achieve higher accuracy. A novel 3D object retrieval method is presented which uses a parameterized annotated model of the shape of the class objects, incorporating its main characteristics. The annotated subdivision-based model is fitted onto objects of the class using a deformable model framework, converted to a geometry image and transformed into the wavelet domain. Object retrieval takes place in the wavelet domain. The method does not require user interaction, achieves high accuracy, is efficient for use with large databases, and is suitable for nonrigid object classes. We apply our method to the face recognition domain, one of the most challenging intraclass retrieval tasks. We used the Face Recognition Grand Challenge v2 database, yielding an average verification rate of 95.2 percent at 10-3 false accept rate. The latest results of our work can be found at http://www.cbl.uh.edu/UR8D/.

  11. SNL3dFace

    SciTech Connect

    Russ, Trina; Koch, Mark; Koudelka, Melissa; Peters, Ralph; Little, Charles; Boehnen, Chris; Peters, Tanya

    2007-07-20

    This software distribution contains MATLAB and C++ code to enable identity verification using 3D images that may or may not contain a texture component. The code is organized to support system performance testing and system capability demonstration through the proper configuration of the available user interface. Using specific algorithm parameters the face recognition system has been demonstrated to achieve a 96.6% verification rate (Pd) at 0.001 false alarm rate. The system computes robust facial features of a 3D normalized face using Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (FLDA). A 3D normalized face is obtained by alighning each face, represented by a set of XYZ coordinated, to a scaled reference face using the Iterative Closest Point (ICP) algorithm. The scaled reference face is then deformed to the input face using an iterative framework with parameters that control the deformed surface regulation an rate of deformation. A variety of options are available to control the information that is encoded by the PCA. Such options include the XYZ coordinates, the difference of each XYZ coordinates from the reference, the Z coordinate, the intensity/texture values, etc. In addition to PCA/FLDA feature projection this software supports feature matching to obtain similarity matrices for performance analysis. In addition, this software supports visualization of the STL, MRD, 2D normalized, and PCA synthetic representations in a 3D environment.

  12. 3D face recognition system using cylindrical hidden-layer neural network: spatial domain and its eigenspace domain

    NASA Astrophysics Data System (ADS)

    Kusumoputro, Benyamin; Pangabean, Martha Y.; Rachman, Leila F.

    2001-09-01

    In this paper, a 3-D face recognition system is developed using a modified neural network. This modified neural network is constructed by substituting each of neuron in its hidden layer of conventional multilayer perceptron with a circular-structure of neurons. This neural system is then called as cylindrical-structure of hidden layer neural network (CHL-NN). The neural system is then applied on a real 3-D face image database that consists of 5 Indonesian persons. The images are taken under four different expressions such as neutral, smile, laugh and free expression. The 2-D images is taken from the human face images by gradually changing visual points, which is done by successively varies the camera position from - 90 to +90 with an interval of 15 degree. The experimental result has shown that the average recognition rate of 60% could be achieved when we used the image in its spatial domain. Improvement of the system is then developed, by transforming the image in its spatial domain into its eigenspace domain. Karhunen Loeve transformation technique is used, and each image in the spatial domain is represented as a point in the eigenspace domain. Fisherface method is then utilized as a feature extraction on the eigenspace domain, and using the same database and experimental procedure, the recognition rate of the system could be increased into 84% in average.

  13. Does face recognition rely on encoding of 3-D surface? Examining the role of shape-from-shading and shape-from-stereo.

    PubMed

    Liu, C H; Collin, C A; Chaudhuri, A

    2000-01-01

    It is now well known that processing of shading information in face recognition is susceptible to bottom lighting and contrast reversal, an effect that may be due to a disruption of 3-D shape processing. The question then is whether the disruption can be rectified by other sources of 3-D information, such as shape-from-stereo. We examined this issue by comparing identification performance either with or without stereo information using top-lit and bottom-lit face stimuli in both photographic positive and negative conditions. The results show that none of the shading effects was reduced by the presence of stereo information. This finding supports the notion that shape-from-shading overrides shape-from-stereo in face perception. Although shape-from-stereo did produce some signs of facilitation for face identification, this effect was negligible. Together, our results support the view that 3-D shape processing plays only a minor role in face recognition. Our data are best accounted for by a weighted function of 2-D processing of shading pattern and 3-D processing of shapes, with a much greater weight assigned to 2-D pattern processing.

  14. Recognition of 3D facial expression from posed data

    NASA Astrophysics Data System (ADS)

    Samad, Manar D.; Iftekharuddin, Khan M.

    2013-05-01

    Although recognition of facial expression in 3D facial images has been an active research area, most of the prior works are limited to using full frontal facial images. These techniques primarily project 3D facial image on 2D and manually select landmarks in 2D projection to extract relevant features. Face recognition in 2D images can be challenging due to unconstrained conditions such as head pose, occlusion, and resulting loss of data. Similarly, pose variation in 3D facial imaging can also result in loss of data. In most of the current 3D facial recognition works, when 3D posed face data are projected onto 2D, additional data loss may render 2D facial expression recognition even more challenging. In comparison, this work proposes novel feature extraction directly from the 3D facial posed images without the need of manual selection of landmarks or projection of images in 2D space. This feature is obtained as the angle between consecutive 3D normal vectors on the vertex points aligned either horizontally or vertically across the 3D facial image. Our facial expression recognition results show that the feature obtained from vertices aligned vertically across the face yields the best accuracy for classification with an average 87.8% area under the ROC. The results further suggest that the same feature outperforms its horizontal counterpart in recognizing facial expressions for pose variation between 35º - 50º with average accuracy of 80% - 60%, respectively.

  15. Thermal infrared exploitation for 3D face reconstruction

    NASA Astrophysics Data System (ADS)

    Abayowa, Bernard O.

    2009-05-01

    Despite the advances in face recognition research, current face recognition systems are still not accurate or robust enough to be deployed in uncontrolled environments. The existence of a pose and illumination invariant face recognition system is still lacking. This research exploits the relationship between thermal infrared and visible imagery, to estimate 3D face with visible texture from infrared imagery. The relationship between visible and thermal infrared texture is learned using kernel canonical correlation analysis(KCCA), and then a 3D modeler is used to estimate the geometric structure from predicted visual imagery. This research will find it's application in uncontrolled environments where illumination and pose invariant identification or tracking is required at long range such as urban search and rescue (Amber alert, missing dementia patient), and manhunt scenarios.

  16. 3D face analysis for demographic biometrics

    SciTech Connect

    Tokola, Ryan A; Mikkilineni, Aravind K; Boehnen, Chris Bensing

    2015-01-01

    Despite being increasingly easy to acquire, 3D data is rarely used for face-based biometrics applications beyond identification. Recent work in image-based demographic biometrics has enjoyed much success, but these approaches suffer from the well-known limitations of 2D representations, particularly variations in illumination, texture, and pose, as well as a fundamental inability to describe 3D shape. This paper shows that simple 3D shape features in a face-based coordinate system are capable of representing many biometric attributes without problem-specific models or specialized domain knowledge. The same feature vector achieves impressive results for problems as diverse as age estimation, gender classification, and race classification.

  17. 3D faces are recognized more accurately and faster than 2D faces, but with similar inversion effects.

    PubMed

    Eng, Z H D; Yick, Y Y; Guo, Y; Xu, H; Reiner, M; Cham, T J; Chen, S H A

    2017-09-01

    Recognition of faces typically occurs via holistic processing where individual features are combined to provide an overall facial representation. However, when faces are inverted, there is greater reliance on featural processing where faces are recognized based on their individual features. These findings are based on a substantial number of studies using 2-dimensional (2D) faces and it is unknown whether these results can be extended to 3-dimensional (3D) faces, which have more depth information that is absent in the typical 2D stimuli used in face recognition literature. The current study used the face inversion paradigm as a means to investigate how holistic and featural processing are differentially influenced by 2D and 3D faces. Twenty-five participants completed a delayed face-matching task consisting of upright and inverted faces that were presented as both 2D and 3D stereoscopic images. Recognition accuracy was significantly higher for 3D upright faces compared to 2D upright faces, providing support that the enriched visual information in 3D stereoscopic images facilitates holistic processing that is essential for the recognition of upright faces. Typical face inversion effects were also obtained, regardless of whether the faces were presented in 2D or 3D. Moreover, recognition performances for 2D inverted and 3D inverted faces did not differ. Taken together, these results demonstrated that 3D stereoscopic effects influence face recognition during holistic processing but not during featural processing. Our findings therefore provide a novel perspective that furthers our understanding of face recognition mechanisms, shedding light on how the integration of stereoscopic information in 3D faces influences face recognition processes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Fabricating 3D figurines with personalized faces.

    PubMed

    Tena, J Rafael; Mahler, Moshe; Beeler, Thabo; Grosse, Max; Hengchin Yeh; Matthews, Iain

    2013-01-01

    We present a semi-automated system for fabricating figurines with faces that are personalised to the individual likeness of the customer. The efficacy of the system has been demonstrated by commercial deployments at Walt Disney World Resort and Star Wars Celebration VI in Orlando Florida. Although the system is semi automated, human intervention is limited to a few simple tasks to maintain the high throughput and consistent quality required for commercial application. In contrast to existing systems that fabricate custom heads that are assembled to pre-fabricated plastic bodies, our system seamlessly integrates 3D facial data with a predefined figurine body into a unique and continuous object that is fabricated as a single piece. The combination of state-of-the-art 3D capture, modelling, and printing that are the core of our system provide the flexibility to fabricate figurines whose complexity is only limited by the creativity of the designer.

  19. 3-D Model Guided Site Recognition

    NASA Astrophysics Data System (ADS)

    Thevenoux, Patrick; Serfaty, Veronique; Zavidovique, Bertrand; Stamon, Georges

    1990-02-01

    Herein is the description of the methodology we adopted to develop a set of algorithms performing the automatic recognition and localisation of sites which are observed through an IR camera from a flying mobile. Considered sites are solid buildings such as houses, power-stations... They must be significant enough to allow satisfactory recognition. However they may include planar subparts like roads, greenfields,... To achieve this recognition, 3D site models are recomputed from CAD models to which are added selected attributes. Chosen models are sets of polyhedral facets which may be processed as derived sets of vertices or edges as well. Polyhedral models are particularly fitting general infrared image properties. Geometrical information is worked from the very beginning of the segmentation process. Image processing procedures extract visual features fitting at best the selected model constituents. At first, a 2D image graph is backprojected into a 3D graph thanks to the model (prediction) and then projection onto the 2D space carries the verification from the generated 3D hypotheses, until matching and localisation are completed. Sporadic monocular images are supposed to be output from an infrared camera. Nevertheless radar images, when available, are concurrently supplied. Provided simple data fusion process, radar information improves greatly the detection of emerging sites and the focus of attention on limited areas of the infrared image, from which the effective recognition is performed. A first implementation of the system is currently under completion relying on edge-based models. Extended use of models allowing feature cooperation is planned and other features like points of interest, regions are already taken into account.

  20. 3D FaceCam: a fast and accurate 3D facial imaging device for biometrics applications

    NASA Astrophysics Data System (ADS)

    Geng, Jason; Zhuang, Ping; May, Patrick; Yi, Steven; Tunnell, David

    2004-08-01

    Human faces are fundamentally three-dimensional (3D) objects, and each face has its unique 3D geometric profile. The 3D geometric features of a human face can be used, together with its 2D texture, for rapid and accurate face recognition purposes. Due to the lack of low-cost and robust 3D sensors and effective 3D facial recognition (FR) algorithms, almost all existing FR systems use 2D face images. Genex has developed 3D solutions that overcome the inherent problems in 2D while also addressing limitations in other 3D alternatives. One important aspect of our solution is a unique 3D camera (the 3D FaceCam) that combines multiple imaging sensors within a single compact device to provide instantaneous, ear-to-ear coverage of a human face. This 3D camera uses three high-resolution CCD sensors and a color encoded pattern projection system. The RGB color information from each pixel is used to compute the range data and generate an accurate 3D surface map. The imaging system uses no moving parts and combines multiple 3D views to provide detailed and complete 3D coverage of the entire face. Images are captured within a fraction of a second and full-frame 3D data is produced within a few seconds. This described method provides much better data coverage and accuracy in feature areas with sharp features or details (such as the nose and eyes). Using this 3D data, we have been able to demonstrate that a 3D approach can significantly improve the performance of facial recognition. We have conducted tests in which we have varied the lighting conditions and angle of image acquisition in the "field." These tests have shown that the matching results are significantly improved when enrolling a 3D image rather than a single 2D image. With its 3D solutions, Genex is working toward unlocking the promise of powerful 3D FR and transferring FR from a lab technology into a real-world biometric solution.

  1. Exploring local regularities for 3D object recognition

    NASA Astrophysics Data System (ADS)

    Tian, Huaiwen; Qin, Shengfeng

    2016-11-01

    In order to find better simplicity measurements for 3D object recognition, a new set of local regularities is developed and tested in a stepwise 3D reconstruction method, including localized minimizing standard deviation of angles(L-MSDA), localized minimizing standard deviation of segment magnitudes(L-MSDSM), localized minimum standard deviation of areas of child faces (L-MSDAF), localized minimum sum of segment magnitudes of common edges (L-MSSM), and localized minimum sum of areas of child face (L-MSAF). Based on their effectiveness measurements in terms of form and size distortions, it is found that when two local regularities: L-MSDA and L-MSDSM are combined together, they can produce better performance. In addition, the best weightings for them to work together are identified as 10% for L-MSDSM and 90% for L-MSDA. The test results show that the combined usage of L-MSDA and L-MSDSM with identified weightings has a potential to be applied in other optimization based 3D recognition methods to improve their efficacy and robustness.

  2. Famous face recognition, face matching, and extraversion.

    PubMed

    Lander, Karen; Poyarekar, Siddhi

    2015-01-01

    It has been previously established that extraverts who are skilled at interpersonal interaction perform significantly better than introverts on a face-specific recognition memory task. In our experiment we further investigate the relationship between extraversion and face recognition, focusing on famous face recognition and face matching. Results indicate that more extraverted individuals perform significantly better on an upright famous face recognition task and show significantly larger face inversion effects. However, our results did not find an effect of extraversion on face matching or inverted famous face recognition.

  3. 3D Face Modeling Using the Multi-Deformable Method

    PubMed Central

    Hwang, Jinkyu; Yu, Sunjin; Kim, Joongrock; Lee, Sangyoun

    2012-01-01

    In this paper, we focus on the problem of the accuracy performance of 3D face modeling techniques using corresponding features in multiple views, which is quite sensitive to feature extraction errors. To solve the problem, we adopt a statistical model-based 3D face modeling approach in a mirror system consisting of two mirrors and a camera. The overall procedure of our 3D facial modeling method has two primary steps: 3D facial shape estimation using a multiple 3D face deformable model and texture mapping using seamless cloning that is a type of gradient-domain blending. To evaluate our method's performance, we generate 3D faces of 30 individuals and then carry out two tests: accuracy test and robustness test. Our method shows not only highly accurate 3D face shape results when compared with the ground truth, but also robustness to feature extraction errors. Moreover, 3D face rendering results intuitively show that our method is more robust to feature extraction errors than other 3D face modeling methods. An additional contribution of our method is that a wide range of face textures can be acquired by the mirror system. By using this texture map, we generate realistic 3D face for individuals at the end of the paper. PMID:23201976

  4. 3D Face modeling using the multi-deformable method.

    PubMed

    Hwang, Jinkyu; Yu, Sunjin; Kim, Joongrock; Lee, Sangyoun

    2012-09-25

    In this paper, we focus on the problem of the accuracy performance of 3D face modeling techniques using corresponding features in multiple views, which is quite sensitive to feature extraction errors. To solve the problem, we adopt a statistical model-based 3D face modeling approach in a mirror system consisting of two mirrors and a camera. The overall procedure of our 3D facial modeling method has two primary steps: 3D facial shape estimation using a multiple 3D face deformable model and texture mapping using seamless cloning that is a type of gradient-domain blending. To evaluate our method's performance, we generate 3D faces of 30 individuals and then carry out two tests: accuracy test and robustness test. Our method shows not only highly accurate 3D face shape results when compared with the ground truth, but also robustness to feature extraction errors. Moreover, 3D face rendering results intuitively show that our method is more robust to feature extraction errors than other 3D face modeling methods. An additional contribution of our method is that a wide range of face textures can be acquired by the mirror system. By using this texture map, we generate realistic 3D face for individuals at the end of the paper.

  5. Toward hyperspectral face recognition

    NASA Astrophysics Data System (ADS)

    Robila, Stefan A.

    2008-02-01

    Face recognition continues to meet significant challenges in reaching accurate results and still remains one of the activities where humans outperform technology. An attractive approach in improving face identification is provided by the fusion of multiple imaging sources such as visible and infrared images. Hyperspectral data, i.e. images collected over hundreds of narrow contiguous light spectrum intervals constitute a natural choice for expanding face recognition image fusion, especially since it may provide information beyond the normal visible range, thus exceeding the normal human sensing. In this paper we investigate the efficiency of hyperspectral face recognition through an in house experiment that collected data in over 120 bands within the visible and near infrared range. The imagery was produced using an off the shelf sensor in both indoors and outdoors with the subjects being photographed from various angles. Further processing included spectra collection and feature extraction. Human matching performance based on spectral properties is discussed.

  6. 3D palmprint data fast acquisition and recognition

    NASA Astrophysics Data System (ADS)

    Wang, Xiaoxu; Huang, Shujun; Gao, Nan; Zhang, Zonghua

    2014-11-01

    This paper presents a fast 3D (Three-Dimension) palmprint capturing system and develops an efficient 3D palmprint feature extraction and recognition method. In order to fast acquire accurate 3D shape and texture of palmprint, a DLP projector triggers a CCD camera to realize synchronization. By generating and projecting green fringe pattern images onto the measured palm surface, 3D palmprint data are calculated from the fringe pattern images. The periodic feature vector can be derived from the calculated 3D palmprint data, so undistorted 3D biometrics is obtained. Using the obtained 3D palmprint data, feature matching test have been carried out by Gabor filter, competition rules and the mean curvature. Experimental results on capturing 3D palmprint show that the proposed acquisition method can fast get 3D shape information of palmprint. Some initial experiments on recognition show the proposed method is efficient by using 3D palmprint data.

  7. Modeling 3D faces from samplings via compressive sensing

    NASA Astrophysics Data System (ADS)

    Sun, Qi; Tang, Yanlong; Hu, Ping

    2013-07-01

    3D data is easier to acquire for family entertainment purpose today because of the mass-production, cheapness and portability of domestic RGBD sensors, e.g., Microsoft Kinect. However, the accuracy of facial modeling is affected by the roughness and instability of the raw input data from such sensors. To overcome this problem, we introduce compressive sensing (CS) method to build a novel 3D super-resolution scheme to reconstruct high-resolution facial models from rough samples captured by Kinect. Unlike the simple frame fusion super-resolution method, this approach aims to acquire compressed samples for storage before a high-resolution image is produced. In this scheme, depth frames are firstly captured and then each of them is measured into compressed samples using sparse coding. Next, the samples are fused to produce an optimal one and finally a high-resolution image is recovered from the fused sample. This framework is able to recover 3D facial model of a given user from compressed simples and this can reducing storage space as well as measurement cost in future devices e.g., single-pixel depth cameras. Hence, this work can potentially be applied into future applications, such as access control system using face recognition, and smart phones with depth cameras, which need high resolution and little measure time.

  8. Infrared face recognition using texture descriptors

    NASA Astrophysics Data System (ADS)

    Akhloufi, Moulay A.; Bendada, Abdelhakim

    2010-05-01

    Face recognition is an area of computer vision that has attracted a lot of interest from the research community. A growing demand for robust face recognition software in security applications has driven the development of interesting approaches in this field. A large quantity of research in face recognition deals with visible face images. In the visible spectrum the illumination and face expressions changes represent a significant challenge for the recognition system. To avoid these problems, researchers proposed recently the use of 3D and infrared imaging for face recognition. In this work, we introduce a new framework for infrared face recognition using texture descriptors. This framework exploits linear and non linear dimensionality reduction techniques for face learning and recognition in the texture space. Active and passive infrared imaging modalities are used and comparison with visible face recognition is performed. Two multispectral face recognition databases were used in our experiments: Equinox Database (Visible, SWIR, MWIR, LWIR) and Laval University Multispectral Database (Visible, NIR, MWIR, LWIR). The obtained results show high increase in recognition performance when texture descriptors like LBP (Local Binary Pattern) and LTP (Local Ternary Pattern) are used. The best result was obtained in the short wave infrared spectrum (SWIR) using non linear dimensionality reduction techniques.

  9. 3-D object recognition using 2-D views.

    PubMed

    Li, Wenjing; Bebis, George; Bourbakis, Nikolaos G

    2008-11-01

    We consider the problem of recognizing 3-D objects from 2-D images using geometric models and assuming different viewing angles and positions. Our goal is to recognize and localize instances of specific objects (i.e., model-based) in a scene. This is in contrast to category-based object recognition methods where the goal is to search for instances of objects that belong to a certain visual category (e.g., faces or cars). The key contribution of our work is improving 3-D object recognition by integrating Algebraic Functions of Views (AFoVs), a powerful framework for predicting the geometric appearance of an object due to viewpoint changes, with indexing and learning. During training, we compute the space of views that groups of object features can produce under the assumption of 3-D linear transformations, by combining a small number of reference views that contain the object features using AFoVs. Unrealistic views (e.g., due to the assumption of 3-D linear transformations) are eliminated by imposing a pair of rigidity constraints based on knowledge of the transformation between the reference views of the object. To represent the space of views that an object can produce compactly while allowing efficient hypothesis generation during recognition, we propose combining indexing with learning in two stages. In the first stage, we sample the space of views of an object sparsely and represent information about the samples using indexing. In the second stage, we build probabilistic models of shape appearance by sampling the space of views of the object densely and learning the manifold formed by the samples. Learning employs the Expectation-Maximization (EM) algorithm and takes place in a "universal," lower-dimensional, space computed through Random Projection (RP). During recognition, we extract groups of point features from the scene and we use indexing to retrieve the most feasible model groups that might have produced them (i.e., hypothesis generation). The likelihood

  10. 3D object recognition based on local descriptors

    NASA Astrophysics Data System (ADS)

    Jakab, Marek; Benesova, Wanda; Racev, Marek

    2015-01-01

    In this paper, we propose an enhanced method of 3D object description and recognition based on local descriptors using RGB image and depth information (D) acquired by Kinect sensor. Our main contribution is focused on an extension of the SIFT feature vector by the 3D information derived from the depth map (SIFT-D). We also propose a novel local depth descriptor (DD) that includes a 3D description of the key point neighborhood. Thus defined the 3D descriptor can then enter the decision-making process. Two different approaches have been proposed, tested and evaluated in this paper. First approach deals with the object recognition system using the original SIFT descriptor in combination with our novel proposed 3D descriptor, where the proposed 3D descriptor is responsible for the pre-selection of the objects. Second approach demonstrates the object recognition using an extension of the SIFT feature vector by the local depth description. In this paper, we present the results of two experiments for the evaluation of the proposed depth descriptors. The results show an improvement in accuracy of the recognition system that includes the 3D local description compared with the same system without the 3D local description. Our experimental system of object recognition is working near real-time.

  11. Recognition Memory for Realistic Synthetic Faces

    PubMed Central

    Yotsumoto, Yuko; Kahana, Michael J.; Wilson, Hugh R.; Sekuler, Robert

    2006-01-01

    A series of experiments examined short-term recognition memory for trios of briefly-presented, synthetic human faces derived from three real human faces. The stimuli were graded series of faces, which differed by varying known amounts from the face of the average female. Faces based on each of the three real faces were transformed so as to lie along orthogonal axes in a 3-D face space. Experiment 1 showed that the synthetic faces' perceptual similarity stucture strongly influenced recognition memory. Results were fit by NEMo, a noisy exemplar model of perceptual recognition memory. The fits revealed that recognition memory was influenced both by the similarity of the probe to series items, and by the similarities among the series items themselves. Non-metric multi-dimensional scaling (MDS) showed that faces' perceptual representations largely preserved the 3-D space in which the face stimuli were arrayed. NEMo gave a better account of the results when similarity was defined as perceptual, MDS similarity rather than physical proximity of one face to another. Experiment 2 confirmed the importance of within-list homogeneity directly, without mediation of a model. We discuss the affinities and differences between visual memory for synthetic faces and memory for simpler stimuli. PMID:17948069

  12. Personal perceptual and cognitive property for 3D recognition

    NASA Astrophysics Data System (ADS)

    Matozaki, Takeshi; Tanisita, Akihiko

    1996-04-01

    3D closed circuit TV which produces stereoscopic vision by observing different images through each eye alternately, has been proposed. But, there are several problems, both physiological and psychological, for 3D image observation in many fields. From this prospective, we are learning personal visual characteristics for 3D recognition in the transition from 2D to 3D. We have separated the mechanism of 3D recognition into several categories, and formed some hypothesis about the personal features. These hypotheses are related to an observer's personal features, as follows: (1) consideration of the angle between the left and the right eye's line of vision and the adjustment of focus, (2) consideration of the angle of vision and the time required for fusion, (3) consideration of depth sense based on life experience, (4) consideration of 3D experience, and (5) consideration of 3D sense based on the observer's age. To establish these hypotheses, and we have analyzed the personal features of the time interval required for 3D recognition through some examinations to examinees. Examinees indicate their response for 3D recognition by pushing a button. Recently, we introduced a method for picking up the reaction of 3D recognition from examinees through their biological information, for example, analysis of pulse waves of the finger. We also bring a hypothesis, as a result of the analysis of pulse waves. (1) We can observe chaotic response when the examinee is recognizing a 2D image. (2) We can observe periodic response when the examinee is recognizing a 3D image. We are making nonlinear forecasts by getting correlation between the forecast and the biological phenomena. Deterministic nonlinear prediction are applied to the data, as a promising method of chaotic time series analysis in order to analyze the long term unpredictability, one of the fundamental characteristics of deterministic chaos.

  13. A 2D range Hausdorff approach to 3D facial recognition.

    SciTech Connect

    Koch, Mark William; Russ, Trina Denise; Little, Charles Quentin

    2004-11-01

    This paper presents a 3D facial recognition algorithm based on the Hausdorff distance metric. The standard 3D formulation of the Hausdorff matching algorithm has been modified to operate on a 2D range image, enabling a reduction in computation from O(N2) to O(N) without large storage requirements. The Hausdorff distance is known for its robustness to data outliers and inconsistent data between two data sets, making it a suitable choice for dealing with the inherent problems in many 3D datasets due to sensor noise and object self-occlusion. For optimal performance, the algorithm assumes a good initial alignment between probe and template datasets. However, to minimize the error between two faces, the alignment can be iteratively refined. Results from the algorithm are presented using 3D face images from the Face Recognition Grand Challenge database version 1.0.

  14. [Comparative studies of face recognition].

    PubMed

    Kawai, Nobuyuki

    2012-07-01

    Every human being is proficient in face recognition. However, the reason for and the manner in which humans have attained such an ability remain unknown. These questions can be best answered-through comparative studies of face recognition in non-human animals. Studies in both primates and non-primates show that not only primates, but also non-primates possess the ability to extract information from their conspecifics and from human experimenters. Neural specialization for face recognition is shared with mammals in distant taxa, suggesting that face recognition evolved earlier than the emergence of mammals. A recent study indicated that a social insect, the golden paper wasp, can distinguish their conspecific faces, whereas a closely related species, which has a less complex social lifestyle with just one queen ruling a nest of underlings, did not show strong face recognition for their conspecifics. Social complexity and the need to differentiate between one another likely led humans to evolve their face recognition abilities.

  15. Adaptive 3D Face Reconstruction from Unconstrained Photo Collections.

    PubMed

    Roth, Joseph; Tong, Yiying; Liu, Xiaoming

    2016-12-07

    Given a photo collection of "unconstrained" face images of one individual captured under a variety of unknown pose, expression, and illumination conditions, this paper presents a method for reconstructing a 3D face surface model of the individual along with albedo information. Unlike prior work on face reconstruction that requires large photo collections, we formulate an approach to adapt to photo collections with a high diversity in both the number of images and the image quality. To achieve this, we incorporate prior knowledge about face shape by fitting a 3D morphable model to form a personalized template, following by using a novel photometric stereo formulation to complete the fine details, under a coarse-to-fine scheme. Our scheme incorporates a structural similarity-based local selection step to help identify a common expression for reconstruction while discarding occluded portions of faces. The evaluation of reconstruction performance is through a novel quality measure, in the absence of ground truth 3D scans. Superior large-scale experimental results are reported on synthetic, Internet, and personal photo collections.

  16. Genetic specificity of face recognition.

    PubMed

    Shakeshaft, Nicholas G; Plomin, Robert

    2015-10-13

    Specific cognitive abilities in diverse domains are typically found to be highly heritable and substantially correlated with general cognitive ability (g), both phenotypically and genetically. Recent twin studies have found the ability to memorize and recognize faces to be an exception, being similarly heritable but phenotypically substantially uncorrelated both with g and with general object recognition. However, the genetic relationships between face recognition and other abilities (the extent to which they share a common genetic etiology) cannot be determined from phenotypic associations. In this, to our knowledge, first study of the genetic associations between face recognition and other domains, 2,000 18- and 19-year-old United Kingdom twins completed tests assessing their face recognition, object recognition, and general cognitive abilities. Results confirmed the substantial heritability of face recognition (61%), and multivariate genetic analyses found that most of this genetic influence is unique and not shared with other cognitive abilities.

  17. Genetic specificity of face recognition

    PubMed Central

    Shakeshaft, Nicholas G.; Plomin, Robert

    2015-01-01

    Specific cognitive abilities in diverse domains are typically found to be highly heritable and substantially correlated with general cognitive ability (g), both phenotypically and genetically. Recent twin studies have found the ability to memorize and recognize faces to be an exception, being similarly heritable but phenotypically substantially uncorrelated both with g and with general object recognition. However, the genetic relationships between face recognition and other abilities (the extent to which they share a common genetic etiology) cannot be determined from phenotypic associations. In this, to our knowledge, first study of the genetic associations between face recognition and other domains, 2,000 18- and 19-year-old United Kingdom twins completed tests assessing their face recognition, object recognition, and general cognitive abilities. Results confirmed the substantial heritability of face recognition (61%), and multivariate genetic analyses found that most of this genetic influence is unique and not shared with other cognitive abilities. PMID:26417086

  18. Reconstructing 3D Face Model with Associated Expression Deformation from a Single Face Image via Constructing a Low-Dimensional Expression Deformation Manifold.

    PubMed

    Wang, Shu-Fan; Lai, Shang-Hong

    2011-10-01

    Facial expression modeling is central to facial expression recognition and expression synthesis for facial animation. In this work, we propose a manifold-based 3D face reconstruction approach to estimating the 3D face model and the associated expression deformation from a single face image. With the proposed robust weighted feature map (RWF), we can obtain the dense correspondences between 3D face models and build a nonlinear 3D expression manifold from a large set of 3D facial expression models. Then a Gaussian mixture model in this manifold is learned to represent the distribution of expression deformation. By combining the merits of morphable neutral face model and the low-dimensional expression manifold, a novel algorithm is developed to reconstruct the 3D face geometry as well as the facial deformation from a single face image in an energy minimization framework. Experimental results on simulated and real images are shown to validate the effectiveness and accuracy of the proposed algorithm.

  19. 3D object recognition in TOF data sets

    NASA Astrophysics Data System (ADS)

    Hess, Holger; Albrecht, Martin; Grothof, Markus; Hussmann, Stephan; Oikonomidis, Nikolaos; Schwarte, Rudolf

    2003-08-01

    In the last years 3D-Vision systems based on the Time-Of-Flight (TOF) principle have gained more importance than Stereo Vision (SV). TOF offers a direct depth-data acquisition, whereas SV involves a great amount of computational power for a comparable 3D data set. Due to the enormous progress in TOF-techniques, nowadays 3D cameras can be manufactured and be used for many practical applications. Hence there is a great demand for new accurate algorithms for 3D object recognition and classification. This paper presents a new strategy and algorithm designed for a fast and solid object classification. A challenging example - accurate classification of a (half-) sphere - demonstrates the performance of the developed algorithm. Finally, the transition from a general model of the system to specific applications such as Intelligent Airbag Control and Robot Assistance in Surgery are introduced. The paper concludes with the current research results in the above mentioned fields.

  20. Detailed 3D representations for object recognition and modeling.

    PubMed

    Zia, M Zeeshan; Stark, Michael; Schiele, Bernt; Schindler, Konrad

    2013-11-01

    Geometric 3D reasoning at the level of objects has received renewed attention recently in the context of visual scene understanding. The level of geometric detail, however, is typically limited to qualitative representations or coarse boxes. This is linked to the fact that today's object class detectors are tuned toward robust 2D matching rather than accurate 3D geometry, encouraged by bounding-box-based benchmarks such as Pascal VOC. In this paper, we revisit ideas from the early days of computer vision, namely, detailed, 3D geometric object class representations for recognition. These representations can recover geometrically far more accurate object hypotheses than just bounding boxes, including continuous estimates of object pose and 3D wireframes with relative 3D positions of object parts. In combination with robust techniques for shape description and inference, we outperform state-of-the-art results in monocular 3D pose estimation. In a series of experiments, we analyze our approach in detail and demonstrate novel applications enabled by such an object class representation, such as fine-grained categorization of cars and bicycles, according to their 3D geometry, and ultrawide baseline matching.

  1. 3D Face Hallucination from a Single Depth Frame

    PubMed Central

    Liang, Shu; Kemelmacher-Shlizerman, Ira; Shapiro, Linda G.

    2015-01-01

    We present an algorithm that takes a single frame of a person’s face from a depth camera, e.g., Kinect, and produces a high-resolution 3D mesh of the input face. We leverage a dataset of 3D face meshes of 1204 distinct individuals ranging from age 3 to 40, captured in a neutral expression. We divide the input depth frame into semantically significant regions (eyes, nose, mouth, cheeks) and search the database for the best matching shape per region. We further combine the input depth frame with the matched database shapes into a single mesh that results in a highresolution shape of the input person. Our system is fully automatic and uses only depth data for matching, making it invariant to imaging conditions. We evaluate our results using ground truth shapes, as well as compare to state-of-the-art shape estimation methods. We demonstrate the robustness of our local matching approach with high-quality reconstruction of faces that fall outside of the dataset span, e.g., faces older than 40 years old, facial expressions, and different ethnicities. PMID:26280021

  2. Holistic processing predicts face recognition.

    PubMed

    Richler, Jennifer J; Cheung, Olivia S; Gauthier, Isabel

    2011-04-01

    The concept of holistic processing is a cornerstone of face-recognition research. In the study reported here, we demonstrated that holistic processing predicts face-recognition abilities on the Cambridge Face Memory Test and on a perceptual face-identification task. Our findings validate a large body of work that relies on the assumption that holistic processing is related to face recognition. These findings also reconcile the study of face recognition with the perceptual-expertise work it inspired; such work links holistic processing of objects with people's ability to individuate them. Our results differ from those of a recent study showing no link between holistic processing and face recognition. This discrepancy can be attributed to the use in prior research of a popular but flawed measure of holistic processing. Our findings salvage the central role of holistic processing in face recognition and cast doubt on a subset of the face-perception literature that relies on a problematic measure of holistic processing.

  3. View combination in recognition of 3-D virtual reality layouts.

    PubMed

    Zhang, Hui; Friedman, Alinda; Mou, Weimin; Waller, David

    2012-12-01

    We investigated whether a normalization model or view combination model fit the performance of scene recognition of 3-D layouts using a virtual-reality paradigm. Participants learned a layout of seven objects from two training views (e.g., 0° and 48°) by discriminating the "correct" layout from distracters. Later, they performed a discrimination task using the training views (e.g., 0° and 48°), an interpolated view (e.g., 24°), an extrapolated view (e.g., 72°), and a far view (e.g., 96°). The results showed that the interpolated view was easier to discriminate than the extrapolated view and even easier than the training views. These results extend the applicability of view combination accounts of recognition to 3-D stimuli with stereoscopic depth information. © 2012 The Institute of Psychology, Chinese Academy of Sciences and Blackwell Publishing Asia Pty Ltd.

  4. Effective indexing for face recognition

    NASA Astrophysics Data System (ADS)

    Sochenkov, I.; Sochenkova, A.; Vokhmintsev, A.; Makovetskii, A.; Melnikov, A.

    2016-09-01

    Face recognition is one of the most important tasks in computer vision and pattern recognition. Face recognition is useful for security systems to provide safety. In some situations it is necessary to identify the person among many others. In this case this work presents new approach in data indexing, which provides fast retrieval in big image collections. Data indexing in this research consists of five steps. First, we detect the area containing face, second we align face, and then we detect areas containing eyes and eyebrows, nose, mouth. After that we find key points of each area using different descriptors and finally index these descriptors with help of quantization procedure. The experimental analysis of this method is performed. This paper shows that performing method has results at the level of state-of-the-art face recognition methods, but it is also gives results fast that is important for the systems that provide safety.

  5. Automated Recognition of 3D Features in GPIR Images

    NASA Technical Reports Server (NTRS)

    Park, Han; Stough, Timothy; Fijany, Amir

    2007-01-01

    A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a

  6. Conformal geometry and its applications on 3D shape matching, recognition, and stitching.

    PubMed

    Wang, Sen; Wang, Yang; Jin, Miao; Gu, Xianfeng David; Samaras, Dimitris

    2007-07-01

    Three-dimensional shape matching is a fundamental issue in computer vision with many applications such as shape registration, 3D object recognition, and classification. However, shape matching with noise, occlusion, and clutter is a challenging problem. In this paper, we analyze a family of quasi-conformal maps including harmonic maps, conformal maps, and least-squares conformal maps with regards to 3D shape matching. As a result, we propose a novel and computationally efficient shape matching framework by using least-squares conformal maps. According to conformal geometry theory, each 3D surface with disk topology can be mapped to a 2D domain through a global optimization and the resulting map is a diffeomorphism, i.e., one-to-one and onto. This allows us to simplify the 3D shape-matching problem to a 2D image-matching problem, by comparing the resulting 2D parametric maps, which are stable, insensitive to resolution changes and robust to occlusion, and noise. Therefore, highly accurate and efficient 3D shape matching algorithms can be achieved by using the above three parametric maps. Finally, the robustness of least-squares conformal maps is evaluated and analyzed comprehensively in 3D shape matching with occlusion, noise, and resolution variation. In order to further demonstrate the performance of our proposed method, we also conduct a series of experiments on two computer vision applications, i.e., 3D face recognition and 3D nonrigid surface alignment and stitching.

  7. GAYE: a face recognition system

    NASA Astrophysics Data System (ADS)

    Kepenekci, Burcu; Tek, F. Boray; Cilingir, Onur; Sakarya, Ufuk; Akar, Gozde B.

    2004-05-01

    In this paper, a new face recognition system, GAYE, is presented. GAYE is a fully automatic system that detects and recognizes faces in cluttered scenes. The input of the system is any digitized image/image sequence that includes face/faces. The basic building blocks of the system are face detection, feature extraction and feature comparison. Face detection is based on skin color segmentation. For feature extraction, a novel approach is proposed that depends on the Gabor wavelet transform of the face image. By comparing facial feature vectors system finally makes a decision if the incoming person is recognized or not. Real time system tests show that GAYE achieves a recognition ratio over %90.

  8. Aesthetic preference recognition of 3D shapes using EEG.

    PubMed

    Chew, Lin Hou; Teo, Jason; Mountstephens, James

    2016-04-01

    Recognition and identification of aesthetic preference is indispensable in industrial design. Humans tend to pursue products with aesthetic values and make buying decisions based on their aesthetic preferences. The existence of neuromarketing is to understand consumer responses toward marketing stimuli by using imaging techniques and recognition of physiological parameters. Numerous studies have been done to understand the relationship between human, art and aesthetics. In this paper, we present a novel preference-based measurement of user aesthetics using electroencephalogram (EEG) signals for virtual 3D shapes with motion. The 3D shapes are designed to appear like bracelets, which is generated by using the Gielis superformula. EEG signals were collected by using a medical grade device, the B-Alert X10 from advance brain monitoring, with a sampling frequency of 256 Hz and resolution of 16 bits. The signals obtained when viewing 3D bracelet shapes were decomposed into alpha, beta, theta, gamma and delta rhythm by using time-frequency analysis, then classified into two classes, namely like and dislike by using support vector machines and K-nearest neighbors (KNN) classifiers respectively. Classification accuracy of up to 80 % was obtained by using KNN with the alpha, theta and delta rhythms as the features extracted from frontal channels, Fz, F3 and F4 to classify two classes, like and dislike.

  9. 3D quantitative analysis of early decomposition changes of the human face.

    PubMed

    Caplova, Zuzana; Gibelli, Daniele Maria; Poppa, Pasquale; Cummaudo, Marco; Obertova, Zuzana; Sforza, Chiarella; Cattaneo, Cristina

    2017-07-13

    Decomposition of the human body and human face is influenced, among other things, by environmental conditions. The early decomposition changes that modify the appearance of the face may hamper the recognition and identification of the deceased. Quantitative assessment of those changes may provide important information for forensic identification. This report presents a pilot 3D quantitative approach of tracking early decomposition changes of a single cadaver in controlled environmental conditions by summarizing the change with weekly morphological descriptions. The root mean square (RMS) value was used to evaluate the changes of the face after death. The results showed a high correlation (r = 0.863) between the measured RMS and the time since death. RMS values of each scan are presented, as well as the average weekly RMS values. The quantification of decomposition changes could improve the accuracy of antemortem facial approximation and potentially could allow the direct comparisons of antemortem and postmortem 3D scans.

  10. Action and gait recognition from recovered 3-D human joints.

    PubMed

    Gu, Junxia; Ding, Xiaoqing; Wang, Shengjin; Wu, Youshou

    2010-08-01

    A common viewpoint-free framework that fuses pose recovery and classification for action and gait recognition is presented in this paper. First, a markerless pose recovery method is adopted to automatically capture the 3-D human joint and pose parameter sequences from volume data. Second, multiple configuration features (combination of joints) and movement features (position, orientation, and height of the body) are extracted from the recovered 3-D human joint and pose parameter sequences. A hidden Markov model (HMM) and an exemplar-based HMM are then used to model the movement features and configuration features, respectively. Finally, actions are classified by a hierarchical classifier that fuses the movement features and the configuration features, and persons are recognized from their gait sequences with the configuration features. The effectiveness of the proposed approach is demonstrated with experiments on the Institut National de Recherche en Informatique et Automatique Xmas Motion Acquisition Sequences data set.

  11. Face Processing: Models For Recognition

    NASA Astrophysics Data System (ADS)

    Turk, Matthew A.; Pentland, Alexander P.

    1990-03-01

    The human ability to process faces is remarkable. We can identify perhaps thousands of faces learned throughout our lifetime and read facial expression to understand such subtle qualities as emotion. These skills are quite robust, despite sometimes large changes in the visual stimulus due to expression, aging, and distractions such as glasses or changes in hairstyle or facial hair. Computers which model and recognize faces will be useful in a variety of applications, including criminal identification, human-computer interface, and animation. We discuss models for representing faces and their applicability to the task of recognition, and present techniques for identifying faces and detecting eye blinks.

  12. 3-D Object Recognition from Point Cloud Data

    NASA Astrophysics Data System (ADS)

    Smith, W.; Walker, A. S.; Zhang, B.

    2011-09-01

    The market for real-time 3-D mapping includes not only traditional geospatial applications but also navigation of unmanned autonomous vehicles (UAVs). Massively parallel processes such as graphics processing unit (GPU) computing make real-time 3-D object recognition and mapping achievable. Geospatial technologies such as digital photogrammetry and GIS offer advanced capabilities to produce 2-D and 3-D static maps using UAV data. The goal is to develop real-time UAV navigation through increased automation. It is challenging for a computer to identify a 3-D object such as a car, a tree or a house, yet automatic 3-D object recognition is essential to increasing the productivity of geospatial data such as 3-D city site models. In the past three decades, researchers have used radiometric properties to identify objects in digital imagery with limited success, because these properties vary considerably from image to image. Consequently, our team has developed software that recognizes certain types of 3-D objects within 3-D point clouds. Although our software is developed for modeling, simulation and visualization, it has the potential to be valuable in robotics and UAV applications. The locations and shapes of 3-D objects such as buildings and trees are easily recognizable by a human from a brief glance at a representation of a point cloud such as terrain-shaded relief. The algorithms to extract these objects have been developed and require only the point cloud and minimal human inputs such as a set of limits on building size and a request to turn on a squaring option. The algorithms use both digital surface model (DSM) and digital elevation model (DEM), so software has also been developed to derive the latter from the former. The process continues through the following steps: identify and group 3-D object points into regions; separate buildings and houses from trees; trace region boundaries; regularize and simplify boundary polygons; construct complex roofs. Several case

  13. Automated Face Recognition System

    DTIC Science & Technology

    1992-12-01

    done at the University of California San Diego will be given(3, 1). Finally, the review will end with a short overview of the Karhunen Lorve and...define a face space. This basis set which is optimally tuned to the training data is derived using the Karhunen Lorve principal component analysis (7

  14. A Taxonomy of 3D Occluded Objects Recognition Techniques

    NASA Astrophysics Data System (ADS)

    Soleimanizadeh, Shiva; Mohamad, Dzulkifli; Saba, Tanzila; Al-ghamdi, Jarallah Saleh

    2016-03-01

    The overall performances of object recognition techniques under different condition (e.g., occlusion, viewpoint, and illumination) have been improved significantly in recent years. New applications and hardware are shifted towards digital photography, and digital media. This faces an increase in Internet usage requiring object recognition for certain applications; particularly occulded objects. However occlusion is still an issue unhandled, interlacing the relations between extracted feature points through image, research is going on to develop efficient techniques and easy to use algorithms that would help users to source images; this need to overcome problems and issues regarding occlusion. The aim of this research is to review recognition occluded objects algorithms and figure out their pros and cons to solve the occlusion problem features, which are extracted from occluded object to distinguish objects from other co-existing objects by determining the new techniques, which could differentiate the occluded fragment and sections inside an image.

  15. Sampling design for face recognition

    NASA Astrophysics Data System (ADS)

    Yan, Yanjun; Osadciw, Lisa A.

    2006-04-01

    A face recognition system consists of two integrated parts: One is the face recognition algorithm, the other is the selected classifier and derived features by the algorithm from a data set. The face recognition algorithm definitely plays a central role, but this paper does not aim at evaluating the algorithm, but deriving the best features for this algorithm from a specific database through sampling design of the training set, which directs how the sample should be collected and dictates the sample space. Sampling design can help exert the full potential of the face recognition algorithm without overhaul. Conventional statistical analysis usually assume some distribution to draw the inference, but the design-based inference does not assume any distribution of the data and it does not assume the independency between the sample observations. The simulations illustrates that the systematic sampling scheme performs better than the simple random sampling scheme, and the systematic sampling is comparable to using all available training images in recognition performance. Meanwhile the sampling schemes can save the system resources and alleviate the overfitting problem. However, the post stratification by sex is not shown to be significant in improving the recognition performance.

  16. Thermal to Visible Face Recognition

    DTIC Science & Technology

    2012-04-01

    recognition has been an active area of research for the past two decades due its wide range of applications in law enforcement and verification...an ideal modality for nighttime tasks, but the large disparateness between the thermal IR and visible spectrums results in a wide modality gap that...CONCLUSION AND FUTURE WORK In this study, we investigated the thermal-to-visible face recognition problem, which has a wide modality gap. We showed

  17. Face recognition for uncontrolled environments

    NASA Astrophysics Data System (ADS)

    Podilchuk, Christine; Hulbert, William; Flachsbart, Ralph; Barinov, Lev

    2010-04-01

    A new face recognition algorithm has been proposed which is robust to variations in pose, expression, illumination and occlusions such as sunglasses. The algorithm is motivated by the Edit Distance used to determine the similarity between strings of one dimensional data such as DNA and text. The key to this approach is how to extend the concept of an Edit Distance on one-dimensional data to two-dimensional image data. The algorithm is based on mapping one image into another and using the characteristics of the mapping to determine a two-dimensional Pictorial-Edit Distance or P-Edit Distance. We show how the properties of the mapping are similar to insertion, deletion and substitution errors defined in an Edit Distance. This algorithm is particularly well suited for face recognition in uncontrolled environments such as stand-off and other surveillance applications. We will describe an entire system designed for face recognition at a distance including face detection, pose estimation, multi-sample fusion of video frames and identification. Here we describe how the algorithm is used for face recognition at a distance, present some initial results and describe future research directions.(

  18. Design of aerosol face masks for children using computerized 3D face analysis.

    PubMed

    Amirav, Israel; Luder, Anthony S; Halamish, Asaf; Raviv, Dan; Kimmel, Ron; Waisman, Dan; Newhouse, Michael T

    2014-08-01

    Aerosol masks were originally developed for adults and downsized for children. Overall fit to minimize dead space and a tight seal are problematic, because children's faces undergo rapid and marked topographic and internal anthropometric changes in their first few months/years of life. Facial three-dimensional (3D) anthropometric data were used to design an optimized pediatric mask. Children's faces (n=271, aged 1 month to 4 years) were scanned with 3D technology. Data for the distance from the bridge of the nose to the tip of the chin (H) and the width of the mouth opening (W) were used to categorize the scans into "small," "medium," and "large" "clusters." "Average" masks were developed from each cluster to provide an optimal seal with minimal dead space. The resulting computerized contour, W and H, were used to develop the SootherMask® that enables children, "suckling" on their own pacifier, to keep the mask on their face, mainly by means of subatmospheric pressure. The relatively wide and flexible rim of the mask accommodates variations in facial size within and between clusters. Unique pediatric face masks were developed based on anthropometric data obtained through computerized 3D face analysis. These masks follow facial contours and gently seal to the child's face, and thus may minimize aerosol leakage and dead space.

  19. Robust feature detection for 3D object recognition and matching

    NASA Astrophysics Data System (ADS)

    Pankanti, Sharath; Dorai, Chitra; Jain, Anil K.

    1993-06-01

    Salient surface features play a central role in tasks related to 3-D object recognition and matching. There is a large body of psychophysical evidence demonstrating the perceptual significance of surface features such as local minima of principal curvatures in the decomposition of objects into a hierarchy of parts. Many recognition strategies employed in machine vision also directly use features derived from surface properties for matching. Hence, it is important to develop techniques that detect surface features reliably. Our proposed scheme consists of (1) a preprocessing stage, (2) a feature detection stage, and (3) a feature integration stage. The preprocessing step selectively smoothes out noise in the depth data without degrading salient surface details and permits reliable local estimation of the surface features. The feature detection stage detects both edge-based and region-based features, of which many are derived from curvature estimates. The third stage is responsible for integrating the information provided by the individual feature detectors. This stage also completes the partial boundaries provided by the individual feature detectors, using proximity and continuity principles of Gestalt. All our algorithms use local support and, therefore, are inherently parallelizable. We demonstrate the efficacy and robustness of our approach by applying it to two diverse domains of applications: (1) segmentation of objects into volumetric primitives and (2) detection of salient contours on free-form surfaces. We have tested our algorithms on a number of real range images with varying degrees of noise and missing data due to self-occlusion. The preliminary results are very encouraging.

  20. Pose-Invariant Face Recognition via RGB-D Images

    PubMed Central

    Sang, Gaoli; Li, Jing; Zhao, Qijun

    2016-01-01

    Three-dimensional (3D) face models can intrinsically handle large pose face recognition problem. In this paper, we propose a novel pose-invariant face recognition method via RGB-D images. By employing depth, our method is able to handle self-occlusion and deformation, both of which are challenging problems in two-dimensional (2D) face recognition. Texture images in the gallery can be rendered to the same view as the probe via depth. Meanwhile, depth is also used for similarity measure via frontalization and symmetric filling. Finally, both texture and depth contribute to the final identity estimation. Experiments on Bosphorus, CurtinFaces, Eurecom, and Kiwi databases demonstrate that the additional depth information has improved the performance of face recognition with large pose variations and under even more challenging conditions. PMID:26819581

  1. Many-faced cells and many-edged faces in 3D Poisson-Voronoi tessellations

    NASA Astrophysics Data System (ADS)

    Hilhorst, H. J.; Lazar, E. A.

    2014-10-01

    Motivated by recent new Monte Carlo data we investigate a heuristic asymptotic theory that applies to n-faced 3D Poisson-Voronoi cells in the limit of large n. We show how this theory may be extended to n-edged cell faces. It predicts the leading order large-n behavior of the average volume and surface area of the n-faced cell, and of the average area and perimeter of the n-edged face. Such a face is shown to be surrounded by a toroidal region of volume n/λ (with λ the seed density) that is void of seeds. Two neighboring cells sharing an n-edged face are found to have their seeds at a typical distance that scales as n-1/6 and whose probability law we determine. We present a new data set of 4 × 109 Monte Carlo generated 3D Poisson-Voronoi cells, larger than any before. Full compatibility is found between the Monte Carlo data and the theory. Deviations from the asymptotic predictions are explained in terms of subleading corrections whose powers in n we estimate from the data.

  2. Creating 3D realistic head: from two orthogonal photos to multiview face contents

    NASA Astrophysics Data System (ADS)

    Lin, Yuan; Lin, Qian; Tang, Feng; Tang, Liang; Lim, Sukhwan; Wang, Shengjin

    2011-03-01

    3D Head models have many applications, such as virtual conference, 3D web game, and so on. The existing several web-based face modeling solutions that can create a 3D face model from one or two user uploaded face images, are limited to generating the 3D model of only face region. The accuracy of such reconstruction is very limited for side views, as well as hair regions. The goal of our research is to develop a framework for reconstructing the realistic 3D human head based on two approximate orthogonal views. Our framework takes two images, and goes through segmentation, feature points detection, 3D bald head reconstruction, 3D hair reconstruction and texture mapping to create a 3D head model. The main contribution of the paper is that the processing steps are applies to both the face region as well as the hair region.

  3. 3D Face Generation Tool Candide for Better Face Matching in Surveillance Video

    DTIC Science & Technology

    2014-07-01

    watch-list screening, biometrics , reliability, performance evaluation Community of Practice: Biometrics and Identity Management Canada Safety and...below. • Dmitry Gorodnichy, Eric Granger “PROVE-IT(FRiV): framework and results”. Also pub- lished in Proceedings of NIST International Biometrics ...Granger, “Evaluation of Face Recognition for Video Surveillance”. Also published in Proceedings of NIST International Biometric Performance Conference

  4. Pose-invariant face-head identification using a bank of neural networks and the 3D neck reference point

    NASA Astrophysics Data System (ADS)

    Hild, Michael; Yoshida, Kazunobu; Hashimoto, Motonobu

    2003-03-01

    A method for recognizing faces in relativley unconstrained environments, such as offices, is described. It can recognize faces occurring over an extended range of orientations and distances relative to the camera. As the pattern recognition mechanism, a bank of small neural networks of the multilayer perceptron type is used, where each perceptron has the task of recognizing only a single person's face. The perceptrons are trained with a set of nine face images representing the nine main facial orientations of the person to be identified, and a set face images from various other persons. The center of the neck is determined as the reference point for face position unification. Geometric normalization and reference point determination utilizes 3-D data point measurements obtained with a stereo camera. The system achieves a recognition rate of about 95%.

  5. Transferring of speech movements from video to 3D face space.

    PubMed

    Pei, Yuru; Zha, Hongbin

    2007-01-01

    We present a novel method for transferring speech animation recorded in low quality videos to high resolution 3D face models. The basic idea is to synthesize the animated faces by an interpolation based on a small set of 3D key face shapes which span a 3D face space. The 3D key shapes are extracted by an unsupervised learning process in 2D video space to form a set of 2D visemes which are then mapped to the 3D face space. The learning process consists of two main phases: 1) Isomap-based nonlinear dimensionality reduction to embed the video speech movements into a low-dimensional manifold and 2) K-means clustering in the low-dimensional space to extract 2D key viseme frames. Our main contribution is that we use the Isomap-based learning method to extract intrinsic geometry of the speech video space and thus to make it possible to define the 3D key viseme shapes. To do so, we need only to capture a limited number of 3D key face models by using a general 3D scanner. Moreover, we also develop a skull movement recovery method based on simple anatomical structures to enhance 3D realism in local mouth movements. Experimental results show that our method can achieve realistic 3D animation effects with a small number of 3D key face models.

  6. 3D facial expression recognition using maximum relevance minimum redundancy geometrical features

    NASA Astrophysics Data System (ADS)

    Rabiu, Habibu; Saripan, M. Iqbal; Mashohor, Syamsiah; Marhaban, Mohd Hamiruce

    2012-12-01

    In recent years, facial expression recognition (FER) has become an attractive research area, which besides the fundamental challenges, it poses, finds application in areas, such as human-computer interaction, clinical psychology, lie detection, pain assessment, and neurology. Generally the approaches to FER consist of three main steps: face detection, feature extraction and expression recognition. The recognition accuracy of FER hinges immensely on the relevance of the selected features in representing the target expressions. In this article, we present a person and gender independent 3D facial expression recognition method, using maximum relevance minimum redundancy geometrical features. The aim is to detect a compact set of features that sufficiently represents the most discriminative features between the target classes. Multi-class one-against-one SVM classifier was employed to recognize the seven facial expressions; neutral, happy, sad, angry, fear, disgust, and surprise. The average recognition accuracy of 92.2% was recorded. Furthermore, inter database homogeneity was investigated between two independent databases the BU-3DFE and UPM-3DFE the results showed a strong homogeneity between the two databases.

  7. Model-based image coding using deformable 3D model for face-to-face communications

    NASA Astrophysics Data System (ADS)

    Cai, Defu; Liang, Huiying; Wang, Xiangwen

    1994-09-01

    The model-based image coding might be the potential method for very/ultra low bit rate visual communications. However, some problems still remain for video practice, such as a finer wireframe 3-D model, precise rule for facial expressions analyzing, and automatic feature points extraction for real time application, etc. This paper proposes a feasible scheme of model-based image coding based on a deformable model which would be suitable for very/ultra low bit rates transmission. Meanwhile, some key techniques, such as automatic face feature point extraction based on a priori knowledge for real time applications and the method of AUs separation of a face on various expressions, is given.

  8. Reading Faces: From Features to Recognition.

    PubMed

    Guntupalli, J Swaroop; Gobbini, M Ida

    2017-09-19

    Chang and Tsao recently reported that the monkey face patch system encodes facial identity in a space of facial features as opposed to exemplars. Here, we discuss how such coding might contribute to face recognition, emphasizing the critical role of learning and interactions with other brain areas for optimizing the recognition of familiar faces. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Photon counting passive 3D image sensing for automatic target recognition.

    PubMed

    Yeom, Seokwon; Javidi, Bahram; Watson, Edward

    2005-11-14

    In this paper, we propose photon counting three-dimensional (3D) passive sensing and object recognition using integral imaging. The application of this approach to 3D automatic target recognition (ATR) is investigated using both linear and nonlinear matched filters. We find there is significant potential of the proposed system for 3D sensing and recognition with a low number of photons. The discrimination capability of the proposed system is quantified in terms of discrimination ratio, Fisher ratio, and receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first report on photon counting 3D passive sensing and ATR with integral imaging.

  10. Distortion-tolerant 3-D object recognition by using single exposure on-axis digital holography.

    PubMed

    Kim, Daesuk; Javidi, Bahram

    2004-11-01

    We present a distortion-tolerant 3-D object recognition system using single exposure on-axis digital holography. In contrast to distortion-tolerant 3-D object recognition employing conventional phase shifting scheme which requires multiple exposures, our proposed method requires only one single digital hologram to be synthesized and used for distortion-tolerant 3-D object recognition. A benefit of the single exposure based on-axis approach is enhanced practicality of digital holography for distortion-tolerant 3-D object recognition in terms of its simplicity and high tolerance to external scene parameters such as moving targets. This paper shows experimentally, that single exposure on-axis digital holography is capable of providing a distortion-tolerant 3-D object recognition capability.

  11. Bayesian Face Recognition and Perceptual Narrowing in Face-Space

    ERIC Educational Resources Information Center

    Balas, Benjamin

    2012-01-01

    During the first year of life, infants' face recognition abilities are subject to "perceptual narrowing", the end result of which is that observers lose the ability to distinguish previously discriminable faces (e.g. other-race faces) from one another. Perceptual narrowing has been reported for faces of different species and different races, in…

  12. Bayesian Face Recognition and Perceptual Narrowing in Face-Space

    ERIC Educational Resources Information Center

    Balas, Benjamin

    2012-01-01

    During the first year of life, infants' face recognition abilities are subject to "perceptual narrowing", the end result of which is that observers lose the ability to distinguish previously discriminable faces (e.g. other-race faces) from one another. Perceptual narrowing has been reported for faces of different species and different races, in…

  13. Configural processing in face recognition in schizophrenia

    PubMed Central

    Schwartz, Barbara L.; Marvel, Cherie L.; Drapalski, Amy; Rosse, Richard B.; Deutsch, Stephen I.

    2006-01-01

    Introduction. There is currently substantial literature to suggest that patients with schizophrenia are impaired on many face-processing tasks. This study investigated the specific effects of configural changes on face recognition in groups of schizophrenia patients. Methods. In Experiment 1, participants identified facial expressions in upright faces and in faces inverted from their upright orientation. Experiments 2 and 3 examined recognition memory for faces and other non-face objects presented in upright and inverted orientations. Experiment 4 explored recognition of facial identity in composite images where the top half of one face was fused to the bottom half of another face to form a new face configuration. Results. In each experiment, the configural change had the same effect on face recognition for the schizophenia patients as it did for control participants. Recognising inverted faces was more difficult than recognising upright faces, with a disproportionate effect of inversion on faces relative to other objects. Recognition of facial identity in face-halves was interfered with by the formation of a new face configuration. Conclusion. Collectively, these results suggest that people with schizophrenia rely on configural information to recognise photographs of faces. PMID:16528403

  14. Affordance-based 3D feature for generic object recognition

    NASA Astrophysics Data System (ADS)

    Iizuka, M.; Akizuki, S.; Hashimoto, M.

    2017-03-01

    Techniques for generic object recognition, which targets everyday objects such as cups and spoons, and techniques for approach vector estimation (e.g. estimating grasp position), which are needed for carrying out tasks involving everyday objects, are considered necessary for the perceptual system of service robots. In this research, we design feature for generic object recognition so they can also be applied to approach vector estimation. To carry out tasks involving everyday objects, estimating the function of the target object is critical. Also, as the function of holding liquid is found in all cups, so a function is shared in each type (class) of everyday objects. We thus propose a generic object recognition method that can estimate the approach vector by expressing an object's function as feature. In a test of the generic object recognition of everyday objects, we confirmed that our proposed method had a 92% recognition rate. This rate was 11% higher than the mainstream generic object recognition technique of using convolutional neural network (CNN).

  15. Challenges Facing 3-D Audio Display Design for Multimedia

    NASA Technical Reports Server (NTRS)

    Begault, Durand R.; Null, Cynthia H. (Technical Monitor)

    1998-01-01

    The challenges facing successful multimedia presentation depend largely on the expectations of the designer and end user for a given application. Perceptual limitations in distance, elevation and azimuth sound source simulation differ significantly between headphone and cross-talk cancellation loudspeaker listening and therefore must be considered. Simulation of an environmental context is desirable but the quality depends on processing resources and lack of interaction with the host acoustical environment. While techniques such as data reduction of head-related transfer functions have been used widely to improve simulation fidelity, another approach involves determining thresholds for environmental acoustic events. Psychoacoustic studies relevant to this approach are reviewed in consideration of multimedia applications

  16. Challenges Facing 3-D Audio Display Design for Multimedia

    NASA Technical Reports Server (NTRS)

    Begault, Durand R.; Null, Cynthia H. (Technical Monitor)

    1998-01-01

    The challenges facing successful multimedia presentation depend largely on the expectations of the designer and end user for a given application. Perceptual limitations in distance, elevation and azimuth sound source simulation differ significantly between headphone and cross-talk cancellation loudspeaker listening and therefore must be considered. Simulation of an environmental context is desirable but the quality depends on processing resources and lack of interaction with the host acoustical environment. While techniques such as data reduction of head-related transfer functions have been used widely to improve simulation fidelity, another approach involves determining thresholds for environmental acoustic events. Psychoacoustic studies relevant to this approach are reviewed in consideration of multimedia applications

  17. Neural microgenesis of personally familiar face recognition.

    PubMed

    Ramon, Meike; Vizioli, Luca; Liu-Shuang, Joan; Rossion, Bruno

    2015-09-01

    Despite a wealth of information provided by neuroimaging research, the neural basis of familiar face recognition in humans remains largely unknown. Here, we isolated the discriminative neural responses to unfamiliar and familiar faces by slowly increasing visual information (i.e., high-spatial frequencies) to progressively reveal faces of unfamiliar or personally familiar individuals. Activation in ventral occipitotemporal face-preferential regions increased with visual information, independently of long-term face familiarity. In contrast, medial temporal lobe structures (perirhinal cortex, amygdala, hippocampus) and anterior inferior temporal cortex responded abruptly when sufficient information for familiar face recognition was accumulated. These observations suggest that following detailed analysis of individual faces in core posterior areas of the face-processing network, familiar face recognition emerges categorically in medial temporal and anterior regions of the extended cortical face network.

  18. Neural microgenesis of personally familiar face recognition

    PubMed Central

    Ramon, Meike; Vizioli, Luca; Liu-Shuang, Joan; Rossion, Bruno

    2015-01-01

    Despite a wealth of information provided by neuroimaging research, the neural basis of familiar face recognition in humans remains largely unknown. Here, we isolated the discriminative neural responses to unfamiliar and familiar faces by slowly increasing visual information (i.e., high-spatial frequencies) to progressively reveal faces of unfamiliar or personally familiar individuals. Activation in ventral occipitotemporal face-preferential regions increased with visual information, independently of long-term face familiarity. In contrast, medial temporal lobe structures (perirhinal cortex, amygdala, hippocampus) and anterior inferior temporal cortex responded abruptly when sufficient information for familiar face recognition was accumulated. These observations suggest that following detailed analysis of individual faces in core posterior areas of the face-processing network, familiar face recognition emerges categorically in medial temporal and anterior regions of the extended cortical face network. PMID:26283361

  19. Traditional facial tattoos disrupt face recognition processes.

    PubMed

    Buttle, Heather; East, Julie

    2010-01-01

    Factors that are important to successful face recognition, such as features, configuration, and pigmentation/reflectance, are all subject to change when a face has been engraved with ink markings. Here we show that the application of facial tattoos, in the form of spiral patterns (typically associated with the Maori tradition of a Moko), disrupts face recognition to a similar extent as face inversion, with recognition accuracy little better than chance performance (2AFC). These results indicate that facial tattoos can severely disrupt our ability to recognise a face that previously did not have the pattern.

  20. Voice Recognition in Face-Blind Patients.

    PubMed

    Liu, Ran R; Pancaroglu, Raika; Hills, Charlotte S; Duchaine, Brad; Barton, Jason J S

    2016-04-01

    Right or bilateral anterior temporal damage can impair face recognition, but whether this is an associative variant of prosopagnosia or part of a multimodal disorder of person recognition is an unsettled question, with implications for cognitive and neuroanatomic models of person recognition. We assessed voice perception and short-term recognition of recently heard voices in 10 subjects with impaired face recognition acquired after cerebral lesions. All 4 subjects with apperceptive prosopagnosia due to lesions limited to fusiform cortex had intact voice discrimination and recognition. One subject with bilateral fusiform and anterior temporal lesions had a combined apperceptive prosopagnosia and apperceptive phonagnosia, the first such described case. Deficits indicating a multimodal syndrome of person recognition were found only in 2 subjects with bilateral anterior temporal lesions. All 3 subjects with right anterior temporal lesions had normal voice perception and recognition, 2 of whom performed normally on perceptual discrimination of faces. This confirms that such lesions can cause a modality-specific associative prosopagnosia.

  1. 3D Object Recognition: Symmetry and Virtual Views

    DTIC Science & Technology

    1992-12-01

    NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATIONI Artificial Intelligence Laboratory REPORT NUMBER 545 Technology Square AIM 1409 Cambridge... ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING A.I. Memo No. 1409 December 1992 C.B.C.L. Paper No. 76 3D Object...research done within the Center for Biological and Computational Learning in the Department of Brain and Cognitive Sciences, and at the Artificial

  2. Feasibility of measurements of valve dimensions in en-face-3D transesophageal echocardiography.

    PubMed

    Eibel, Sarah; Turton, Edwin; Mukherjee, Chirojit; Bevilacqua, Carmine; Ender, Joerg

    2017-05-09

    Newest 3D software allows measurements directly in the en-face-3D TEE mode. Aim of the study was to ascertain whether measurements performed in the en-face-3D TEE mode are comparable with conventional measurement methods based on 2D TEE and 3D using the multiple plane reconstruction mode with the Qlab(®) software. En-face-3D TEE is used more frequently in daily clinical routine during cardiac operations. So far measurements could only be done based on 2D images or with the use of multi planar reconstruction mode with additional software. Measurement directly in the 3D image (en-face-3D TEE) would make measurements faster and easier to use in clinical practice. After approval by the local ethic committee and written informed consent from the patients additionally to a comprehensive perioperative 2D TEE examination a real time (RT) 3D zoom- dataset was recorded. Routine measurements of the length of anterior and posterior mitral valve leaflets as well as mitral valve and aortic valve areas were performed in en-face-3D TEE, multiplanar reconstruction mode using Qlab(®)-software (Philips, Netherlands) and 2D TEE standard views. Twenty nine patients with a mean age of 67 years undergoing elective cardiac surgery/interventions were enrolled in this study. Direct measurements in en-face-3D TEE mode lead to non significant underestimation of all parameters as compared to Qlab(®) and 2D TEE measurements. Measurements in en-face-3D TEE are feasible but lead to non significant underestimation compared to measurements performed with Qlab(®) or in 2D TEE views.

  3. The hierarchical brain network for face recognition.

    PubMed

    Zhen, Zonglei; Fang, Huizhen; Liu, Jia

    2013-01-01

    Numerous functional magnetic resonance imaging (fMRI) studies have identified multiple cortical regions that are involved in face processing in the human brain. However, few studies have characterized the face-processing network as a functioning whole. In this study, we used fMRI to identify face-selective regions in the entire brain and then explore the hierarchical structure of the face-processing network by analyzing functional connectivity among these regions. We identified twenty-five regions mainly in the occipital, temporal and frontal cortex that showed a reliable response selective to faces (versus objects) across participants and across scan sessions. Furthermore, these regions were clustered into three relatively independent sub-networks in a face-recognition task on the basis of the strength of functional connectivity among them. The functionality of the sub-networks likely corresponds to the recognition of individual identity, retrieval of semantic knowledge and representation of emotional information. Interestingly, when the task was switched to object recognition from face recognition, the functional connectivity between the inferior occipital gyrus and the rest of the face-selective regions were significantly reduced, suggesting that this region may serve as an entry node in the face-processing network. In sum, our study provides empirical evidence for cognitive and neural models of face recognition and helps elucidate the neural mechanisms underlying face recognition at the network level.

  4. The Hierarchical Brain Network for Face Recognition

    PubMed Central

    Zhen, Zonglei; Fang, Huizhen; Liu, Jia

    2013-01-01

    Numerous functional magnetic resonance imaging (fMRI) studies have identified multiple cortical regions that are involved in face processing in the human brain. However, few studies have characterized the face-processing network as a functioning whole. In this study, we used fMRI to identify face-selective regions in the entire brain and then explore the hierarchical structure of the face-processing network by analyzing functional connectivity among these regions. We identified twenty-five regions mainly in the occipital, temporal and frontal cortex that showed a reliable response selective to faces (versus objects) across participants and across scan sessions. Furthermore, these regions were clustered into three relatively independent sub-networks in a face-recognition task on the basis of the strength of functional connectivity among them. The functionality of the sub-networks likely corresponds to the recognition of individual identity, retrieval of semantic knowledge and representation of emotional information. Interestingly, when the task was switched to object recognition from face recognition, the functional connectivity between the inferior occipital gyrus and the rest of the face-selective regions were significantly reduced, suggesting that this region may serve as an entry node in the face-processing network. In sum, our study provides empirical evidence for cognitive and neural models of face recognition and helps elucidate the neural mechanisms underlying face recognition at the network level. PMID:23527282

  5. Face recognition increases during saccade preparation.

    PubMed

    Lin, Hai; Rizak, Joshua D; Ma, Yuan-ye; Yang, Shang-chuan; Chen, Lin; Hu, Xin-tian

    2014-01-01

    Face perception is integral to human perception system as it underlies social interactions. Saccadic eye movements are frequently made to bring interesting visual information, such as faces, onto the fovea for detailed processing. Just before eye movement onset, the processing of some basic features, such as the orientation, of an object improves at the saccade landing point. Interestingly, there is also evidence that indicates faces are processed in early visual processing stages similar to basic features. However, it is not known whether this early enhancement of processing includes face recognition. In this study, three experiments were performed to map the timing of face presentation to the beginning of the eye movement in order to evaluate pre-saccadic face recognition. Faces were found to be similarly processed as simple objects immediately prior to saccadic movements. Starting ∼ 120 ms before a saccade to a target face, independent of whether or not the face was surrounded by other faces, the face recognition gradually improved and the critical spacing of the crowding decreased as saccade onset was approaching. These results suggest that an upcoming saccade prepares the visual system for new information about faces at the saccade landing site and may reduce the background in a crowd to target the intended face. This indicates an important role of pre-saccadic eye movement signals in human face recognition.

  6. Face Recognition Performance: Role of Demographic Information

    DTIC Science & Technology

    2012-01-01

    BTAS). His other research interests include pattern recognition and computer vision . Mark J. Burge is a scientist with The MITRE Corporation, McLean... Pattern Anal. Mach. Intell., vol. 28, no. 12, pp. 2037–2041, 2006. [23] X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition ...wavelets for face recognition ,” Pattern Analysis & Applications, vol. 9, pp. 273–292, 2006. [25] M. Riesenhuber and T. Poggio, “Hierarchical models of

  7. 2D DOST based local phase pattern for face recognition

    NASA Astrophysics Data System (ADS)

    Moniruzzaman, Md.; Alam, Mohammad S.

    2017-05-01

    A new two dimensional (2-D) Discrete Orthogonal Stcokwell Transform (DOST) based Local Phase Pattern (LPP) technique has been proposed for efficient face recognition. The proposed technique uses 2-D DOST as preliminary preprocessing and local phase pattern to form robust feature signature which can effectively accommodate various 3D facial distortions and illumination variations. The S-transform, is an extension of the ideas of the continuous wavelet transform (CWT), is also known for its local spectral phase properties in time-frequency representation (TFR). It provides a frequency dependent resolution of the time-frequency space and absolutely referenced local phase information while maintaining a direct relationship with the Fourier spectrum which is unique in TFR. After utilizing 2-D Stransform as the preprocessing and build local phase pattern from extracted phase information yield fast and efficient technique for face recognition. The proposed technique shows better correlation discrimination compared to alternate pattern recognition techniques such as wavelet or Gabor based face recognition. The performance of the proposed method has been tested using the Yale and extended Yale facial database under different environments such as illumination variation and 3D changes in facial expressions. Test results show that the proposed technique yields better performance compared to alternate time-frequency representation (TFR) based face recognition techniques.

  8. Ligand Electron Density Shape Recognition Using 3D Zernike Descriptors

    NASA Astrophysics Data System (ADS)

    Gunasekaran, Prasad; Grandison, Scott; Cowtan, Kevin; Mak, Lora; Lawson, David M.; Morris, Richard J.

    We present a novel approach to crystallographic ligand density interpretation based on Zernike shape descriptors. Electron density for a bound ligand is expanded in an orthogonal polynomial series (3D Zernike polynomials) and the coefficients from this expansion are employed to construct rotation-invariant descriptors. These descriptors can be compared highly efficiently against large databases of descriptors computed from other molecules. In this manuscript we describe this process and show initial results from an electron density interpretation study on a dataset containing over a hundred OMIT maps. We could identify the correct ligand as the first hit in about 30 % of the cases, within the top five in a further 30 % of the cases, and giving rise to an 80 % probability of getting the correct ligand within the top ten matches. In all but a few examples, the top hit was highly similar to the correct ligand in both shape and chemistry. Further extensions and intrinsic limitations of the method are discussed.

  9. Optoelectronic-based face recognition versus electronic PCA-based face recognition

    NASA Astrophysics Data System (ADS)

    Alsamman, A.

    2003-11-01

    Face recognition based on principal component analysis (PCA) using eigenfaces is popular in face recognition markets. In this paper we present a comparison between various optoelectronic face recognition techniques and principal component analysis (PCA) based technique for face recognition. Computer simulations are used to study the effectiveness of PCA based technique especially for facial images with a high level of distortion. Results are then compared to various distortion-invariant optoelectronic face recognition algorithms such as synthetic discriminant functions (SDF), projection-slice SDF, optical correlator based neural networks, and pose estimation based correlation.

  10. Face photo-sketch synthesis and recognition.

    PubMed

    Wang, Xiaogang; Tang, Xiaoou

    2009-11-01

    In this paper, we propose a novel face photo-sketch synthesis and recognition method using a multiscale Markov Random Fields (MRF) model. Our system has three components: 1) given a face photo, synthesizing a sketch drawing; 2) given a face sketch drawing, synthesizing a photo; and 3) searching for face photos in the database based on a query sketch drawn by an artist. It has useful applications for both digital entertainment and law enforcement. We assume that faces to be studied are in a frontal pose, with normal lighting and neutral expression, and have no occlusions. To synthesize sketch/photo images, the face region is divided into overlapping patches for learning. The size of the patches decides the scale of local face structures to be learned. From a training set which contains photo-sketch pairs, the joint photo-sketch model is learned at multiple scales using a multiscale MRF model. By transforming a face photo to a sketch (or transforming a sketch to a photo), the difference between photos and sketches is significantly reduced, thus allowing effective matching between the two in face sketch recognition. After the photo-sketch transformation, in principle, most of the proposed face photo recognition approaches can be applied to face sketch recognition in a straightforward way. Extensive experiments are conducted on a face sketch database including 606 faces, which can be downloaded from our Web site (http://mmlab.ie.cuhk.edu.hk/facesketch.html).

  11. Quest Hierarchy for Hyperspectral Face Recognition

    DTIC Science & Technology

    2011-03-01

    Recognition Rate for Eigenfaces, Eigenfeatures and Combined [24] 41 Neural Networks A promising approach for complex pattern... recognition is the application of neural networks (NN). Given the dimensionality of the face recognition problem and the desire to recreate the human... recognition with only a small sample of stored images for an individual. By using a 2D log polar Gabor transform within an artificial neural network

  12. Optimal-tradeoff circular harmonic function filters for 3D target recognition

    NASA Astrophysics Data System (ADS)

    Vijaya Kumar, Bhagavatula V. K.; Xie, Chunyan; Mahalanobis, Abhijit

    2003-09-01

    3D target recognition is of significant interest because representing the object in 3D space couuld essentially provide a solution to pose variation and self-occlusion problems that are big challenges in 2D pattern recognition. Correlation filers have been used in a variety of 2D pattern matching applications and many correlation filter designs have been developed to handle problems such as rotations. Correlation filters also offer other benefits such as shift-invariance, graceful degradation and closed-form solutions. The 3D extension of correlation filter is a natural extension to handle 3D pattern recognition problem. In this paper, we propose a 3D correlation filter design method based on cylindrical circular harmonic function (CCHF) and use LADAR imagery to illustrate the good performance of CCHF filters.

  13. The advantages of stereo vision in a face recognition system

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng; Blasch, Erik

    2014-06-01

    Humans can recognize a face with binocular vision, while computers typically use a single face image. It is known that the performance of face recognition (by a computer) can be improved using the score fusion of multimodal images and multiple algorithms. A question is: Can we apply stereo vision to a face recognition system? We know that human binocular vision has many advantages such as stereopsis (3D vision), binocular summation, and singleness of vision including fusion of binocular images (cyclopean image). For face recognition, a 3D face or 3D facial features are typically computed from a pair of stereo images. In human visual processes, the binocular summation and singleness of vision are similar as image fusion processes. In this paper, we propose an advanced face recognition system with stereo imaging capability, which is comprised of two 2-in-1 multispectral (visible and thermal) cameras and three recognition algorithms (circular Gaussian filter, face pattern byte, and linear discriminant analysis [LDA]). Specifically, we present and compare stereo fusion at three levels (images, features, and scores) by using stereo images (from left camera and right camera). Image fusion is achieved with three methods (Laplacian pyramid, wavelet transform, average); feature fusion is done with three logical operations (AND, OR, XOR); and score fusion is implemented with four classifiers (LDA, k-nearest neighbor, support vector machine, binomial logical regression). The system performance is measured by probability of correct classification (PCC) rate (reported as accuracy rate in this paper) and false accept rate (FAR). The proposed approaches were validated with a multispectral stereo face dataset from 105 subjects. Experimental results show that any type of stereo fusion can improve the PCC, meanwhile reduce the FAR. It seems that stereo image/feature fusion is superior to stereo score fusion in terms of recognition performance. Further score fusion after image

  14. Face recognition system and method using face pattern words and face pattern bytes

    DOEpatents

    Zheng, Yufeng

    2014-12-23

    The present invention provides a novel system and method for identifying individuals and for face recognition utilizing facial features for face identification. The system and method of the invention comprise creating facial features or face patterns called face pattern words and face pattern bytes for face identification. The invention also provides for pattern recognitions for identification other than face recognition. The invention further provides a means for identifying individuals based on visible and/or thermal images of those individuals by utilizing computer software implemented by instructions on a computer or computer system and a computer readable medium containing instructions on a computer system for face recognition and identification.

  15. Extraversion predicts individual differences in face recognition.

    PubMed

    Li, Jingguang; Tian, Moqian; Fang, Huizhen; Xu, Miao; Li, He; Liu, Jia

    2010-07-01

    In daily life, one of the most common social tasks we perform is to recognize faces. However, the relation between face recognition ability and social activities is largely unknown. Here we ask whether individuals with better social skills are also better at recognizing faces. We found that extraverts who have better social skills correctly recognized more faces than introverts. However, this advantage was absent when extraverts were asked to recognize non-social stimuli (e.g., flowers). In particular, the underlying facet that makes extraverts better face recognizers is the gregariousness facet that measures the degree of inter-personal interaction. In addition, the link between extraversion and face recognition ability was independent of general cognitive abilities. These findings provide the first evidence that links face recognition ability to our daily activity in social communication, supporting the hypothesis that extraverts are better at decoding social information than introverts.

  16. Face recognition motivated by human approach

    NASA Astrophysics Data System (ADS)

    Kamgar-Parsi, Behrooz; Lawson, Wallace Edgar; Kamgar-Parsi, Behzad

    2010-04-01

    We report the development of a face recognition system which operates in the same way as humans in that it is capable of recognizing a number of people, while rejecting everybody else as strangers. While humans do it routinely, a particularly challenging aspect of the problem of open-world face recognition has been the question of rejecting previously unseen faces as unfamiliar. Our approach can handle previously unseen faces; it is based on identifying and enclosing the region(s) in the human face space which belong to the target person(s).

  17. Recognition of Unfamiliar Talking Faces at Birth

    ERIC Educational Resources Information Center

    Coulon, Marion; Guellai, Bahia; Streri, Arlette

    2011-01-01

    Sai (2005) investigated the role of speech in newborns' recognition of their mothers' faces. Her results revealed that, when presented with both their mother's face and that of a stranger, newborns preferred looking at their mother only if she had previously talked to them. The present study attempted to extend these findings to any other faces.…

  18. Automatic face recognition in HDR imaging

    NASA Astrophysics Data System (ADS)

    Pereira, Manuela; Moreno, Juan-Carlos; Proença, Hugo; Pinheiro, António M. G.

    2014-05-01

    The gaining popularity of the new High Dynamic Range (HDR) imaging systems is raising new privacy issues caused by the methods used for visualization. HDR images require tone mapping methods for an appropriate visualization on conventional and non-expensive LDR displays. These visualization methods might result in completely different visualization raising several issues on privacy intrusion. In fact, some visualization methods result in a perceptual recognition of the individuals, while others do not even show any identity. Although perceptual recognition might be possible, a natural question that can rise is how computer based recognition will perform using tone mapping generated images? In this paper, a study where automatic face recognition using sparse representation is tested with images that result from common tone mapping operators applied to HDR images. Its ability for the face identity recognition is described. Furthermore, typical LDR images are used for the face recognition training.

  19. Bayesian face recognition and perceptual narrowing in face-space.

    PubMed

    Balas, Benjamin

    2012-07-01

    During the first year of life, infants' face recognition abilities are subject to 'perceptual narrowing', the end result of which is that observers lose the ability to distinguish previously discriminable faces (e.g. other-race faces) from one another. Perceptual narrowing has been reported for faces of different species and different races, in developing humans and primates. Though the phenomenon is highly robust and replicable, there have been few efforts to model the emergence of perceptual narrowing as a function of the accumulation of experience with faces during infancy. The goal of the current study is to examine how perceptual narrowing might manifest as statistical estimation in 'face-space', a geometric framework for describing face recognition that has been successfully applied to adult face perception. Here, I use a computer vision algorithm for Bayesian face recognition to study how the acquisition of experience in face-space and the presence of race categories affect performance for own and other-race faces. Perceptual narrowing follows from the establishment of distinct race categories, suggesting that the acquisition of category boundaries for race is a key computational mechanism in developing face expertise. © 2012 Blackwell Publishing Ltd.

  20. Bayesian Face Recognition and Perceptual Narrowing in Face-Space

    PubMed Central

    Balas, Benjamin

    2012-01-01

    During the first year of life, infants’ face recognition abilities are subject to “perceptual narrowing,” the end result of which is that observers lose the ability to distinguish previously discriminable faces (e.g. other-race faces) from one another. Perceptual narrowing has been reported for faces of different species and different races, in developing humans and primates. Though the phenomenon is highly robust and replicable, there have been few efforts to model the emergence of perceptual narrowing as a function of the accumulation of experience with faces during infancy. The goal of the current study is to examine how perceptual narrowing might manifest as statistical estimation in “face space,” a geometric framework for describing face recognition that has been successfully applied to adult face perception. Here, I use a computer vision algorithm for Bayesian face recognition to study how the acquisition of experience in face space and the presence of race categories affect performance for own and other-race faces. Perceptual narrowing follows from the establishment of distinct race categories, suggesting that the acquisition of category boundaries for race is a key computational mechanism in developing face expertise. PMID:22709406

  1. Establishing point correspondence of 3D faces via sparse facial deformable model.

    PubMed

    Pan, Gang; Zhang, Xiaobo; Wang, Yueming; Hu, Zhenfang; Zheng, Xiaoxiang; Wu, Zhaohui

    2013-11-01

    Establishing a dense vertex-to-vertex anthropometric correspondence between 3D faces is an important and fundamental problem in 3D face research, which can contribute to most applications of 3D faces. This paper proposes a sparse facial deformable model to automatically achieve this task. For an input 3D face, the basic idea is to generate a new 3D face that has the same mesh topology as a reference face and the highly similar shape to the input face, and whose vertices correspond to those of the reference face in an anthropometric sense. Two constraints: 1) the shape constraint and 2) correspondence constraint are modeled in our method to satisfy the three requirements. The shape constraint is solved by a novel face deformation approach in which a normal-ray scheme is integrated to the closest-vertex scheme to keep high-curvature shapes in deformation. The correspondence constraint is based on an assumption that if the vertices on 3D faces are corresponded, their shape signals lie on a manifold and each face signal can be represented sparsely by a few typical items in a dictionary. The dictionary can be well learnt and contains the distribution information of the corresponded vertices. The correspondence information can be conveyed to the sparse representation of the generated 3D face. Thus, a patch-based sparse representation is proposed as the correspondence constraint. By solving the correspondence constraint iteratively, the vertices of the generated face can be adjusted to correspondence positions gradually. At the early iteration steps, smaller sparsity thresholds are set that yield larger representation errors but better globally corresponded vertices. At the later steps, relatively larger sparsity thresholds are used to encode local shapes. By this method, the vertices in the new face approach the right positions progressively until the final global correspondence is reached. Our method is automatic, and the manual work is needed only in training procedure

  2. Contextual Modulation of Biases in Face Recognition

    PubMed Central

    Felisberti, Fatima Maria; Pavey, Louisa

    2010-01-01

    Background The ability to recognize the faces of potential cooperators and cheaters is fundamental to social exchanges, given that cooperation for mutual benefit is expected. Studies addressing biases in face recognition have so far proved inconclusive, with reports of biases towards faces of cheaters, biases towards faces of cooperators, or no biases at all. This study attempts to uncover possible causes underlying such discrepancies. Methodology and Findings Four experiments were designed to investigate biases in face recognition during social exchanges when behavioral descriptors (prosocial, antisocial or neutral) embedded in different scenarios were tagged to faces during memorization. Face recognition, measured as accuracy and response latency, was tested with modified yes-no, forced-choice and recall tasks (N = 174). An enhanced recognition of faces tagged with prosocial descriptors was observed when the encoding scenario involved financial transactions and the rules of the social contract were not explicit (experiments 1 and 2). Such bias was eliminated or attenuated by making participants explicitly aware of “cooperative”, “cheating” and “neutral/indifferent” behaviors via a pre-test questionnaire and then adding such tags to behavioral descriptors (experiment 3). Further, in a social judgment scenario with descriptors of salient moral behaviors, recognition of antisocial and prosocial faces was similar, but significantly better than neutral faces (experiment 4). Conclusion The results highlight the relevance of descriptors and scenarios of social exchange in face recognition, when the frequency of prosocial and antisocial individuals in a group is similar. Recognition biases towards prosocial faces emerged when descriptors did not state the rules of a social contract or the moral status of a behavior, and they point to the existence of broad and flexible cognitive abilities finely tuned to minor changes in social context. PMID:20886086

  3. Viewpoint Invariant Gesture Recognition and 3D Hand Pose Estimation Using RGB-D

    ERIC Educational Resources Information Center

    Doliotis, Paul

    2013-01-01

    The broad application domain of the work presented in this thesis is pattern classification with a focus on gesture recognition and 3D hand pose estimation. One of the main contributions of the proposed thesis is a novel method for 3D hand pose estimation using RGB-D. Hand pose estimation is formulated as a database retrieval problem. The proposed…

  4. Viewpoint Invariant Gesture Recognition and 3D Hand Pose Estimation Using RGB-D

    ERIC Educational Resources Information Center

    Doliotis, Paul

    2013-01-01

    The broad application domain of the work presented in this thesis is pattern classification with a focus on gesture recognition and 3D hand pose estimation. One of the main contributions of the proposed thesis is a novel method for 3D hand pose estimation using RGB-D. Hand pose estimation is formulated as a database retrieval problem. The proposed…

  5. Face recognition with intensified NIR imagery

    NASA Astrophysics Data System (ADS)

    Socolinsky, Diego A.; Wolff, Lawrence B.; Lundberg, Andrew J.

    2006-04-01

    This paper presents a systematic study of face recognition performance as a function of light level using intensified near infrared imagery. This technology is the most prevalent in both civilian and military night vision equipment, and provides enough intensification for human operators to perform standard tasks under extremely low-light conditions. We describe a comprehensive data collection effort undertaken by the authors to image subjects under carefully controlled illumination and quantify the performance of standard face recognition algorithms on visible and intensified imagery as a function of light level. Performance comparisons for automatic face recognition are reported using the standardized implementations from the CSU Face Identification Evaluation System. The results contained in this paper should constitute the initial step for analysis and deployment of face recognition systems designed to work in low-light level conditions.

  6. New neural-networks-based 3D object recognition system

    NASA Astrophysics Data System (ADS)

    Abolmaesumi, Purang; Jahed, M.

    1997-09-01

    Three-dimensional object recognition has always been one of the challenging fields in computer vision. In recent years, Ulman and Basri (1991) have proposed that this task can be done by using a database of 2-D views of the objects. The main problem in their proposed system is that the correspondent points should be known to interpolate the views. On the other hand, their system should have a supervisor to decide which class does the represented view belong to. In this paper, we propose a new momentum-Fourier descriptor that is invariant to scale, translation, and rotation. This descriptor provides the input feature vectors to our proposed system. By using the Dystal network, we show that the objects can be classified with over 95% precision. We have used this system to classify the objects like cube, cone, sphere, torus, and cylinder. Because of the nature of the Dystal network, this system reaches to its stable point by a single representation of the view to the system. This system can also classify the similar views to a single class (e.g., for the cube, the system generated 9 different classes for 50 different input views), which can be used to select an optimum database of training views. The system is also very flexible to the noise and deformed views.

  7. Face liveness detection for face recognition based on cardiac features of skin color image

    NASA Astrophysics Data System (ADS)

    Suh, Kun Ha; Lee, Eui Chul

    2016-07-01

    With the growth of biometric technology, spoofing attacks have been emerged a threat to the security of the system. Main spoofing scenarios in the face recognition system include the printing attack, replay attack, and 3D mask attack. To prevent such attacks, techniques that evaluating liveness of the biometric data can be considered as a solution. In this paper, a novel face liveness detection method based on cardiac signal extracted from face is presented. The key point of proposed method is that the cardiac characteristic is detected in live faces but not detected in non-live faces. Experimental results showed that the proposed method can be effective way for determining printing attack or 3D mask attack.

  8. Real-time, face recognition technology

    SciTech Connect

    Brady, S.

    1995-11-01

    The Institute for Scientific Computing Research (ISCR) at Lawrence Livermore National Laboratory recently developed the real-time, face recognition technology KEN. KEN uses novel imaging devices such as silicon retinas developed at Caltech or off-the-shelf CCD cameras to acquire images of a face and to compare them to a database of known faces in a robust fashion. The KEN-Online project makes that recognition technology accessible through the World Wide Web (WWW), an internet service that has recently seen explosive growth. A WWW client can submit face images, add them to the database of known faces and submit other pictures that the system tries to recognize. KEN-Online serves to evaluate the recognition technology and grow a large face database. KEN-Online includes the use of public domain tools such as mSQL for its name-database and perl scripts to assist the uploading of images.

  9. Uniform Local Binary Pattern Based Texture-Edge Feature for 3D Human Behavior Recognition.

    PubMed

    Ming, Yue; Wang, Guangchao; Fan, Chunxiao

    2015-01-01

    With the rapid development of 3D somatosensory technology, human behavior recognition has become an important research field. Human behavior feature analysis has evolved from traditional 2D features to 3D features. In order to improve the performance of human activity recognition, a human behavior recognition method is proposed, which is based on a hybrid texture-edge local pattern coding feature extraction and integration of RGB and depth videos information. The paper mainly focuses on background subtraction on RGB and depth video sequences of behaviors, extracting and integrating historical images of the behavior outlines, feature extraction and classification. The new method of 3D human behavior recognition has achieved the rapid and efficient recognition of behavior videos. A large number of experiments show that the proposed method has faster speed and higher recognition rate. The recognition method has good robustness for different environmental colors, lightings and other factors. Meanwhile, the feature of mixed texture-edge uniform local binary pattern can be used in most 3D behavior recognition.

  10. Face Recognition in Humans and Machines

    NASA Astrophysics Data System (ADS)

    O'Toole, Alice; Tistarelli, Massimo

    The study of human face recognition by psychologists and neuroscientists has run parallel to the development of automatic face recognition technologies by computer scientists and engineers. In both cases, there are analogous steps of data acquisition, image processing, and the formation of representations that can support the complex and diverse tasks we accomplish with faces. These processes can be understood and compared in the context of their neural and computational implementations. In this chapter, we present the essential elements of face recognition by humans and machines, taking a perspective that spans psychological, neural, and computational approaches. From the human side, we overview the methods and techniques used in the neurobiology of face recognition, the underlying neural architecture of the system, the role of visual attention, and the nature of the representations that emerges. From the computational side, we discuss face recognition technologies and the strategies they use to overcome challenges to robust operation over viewing parameters. Finally, we conclude the chapter with a look at some recent studies that compare human and machine performances at face recognition.

  11. Recognition memory for faces: when familiarity supports associative recognition judgments.

    PubMed

    Yonelinas, A P; Kroll, N E; Dobbins, I G; Soltani, M

    1999-12-01

    Recognition memory for single items can be dissociated from recognition memory for the associations between items. For example, recognition tests for single words produce curvilinear receiver operating characteristics (ROCs), but associative recognition tests for word pairs produce linear ROCs. These dissociations are consistent with dual-process theories of recognition and suggest that associative recognition relies on recollection but that item recognition relies on a combination of recollection and assessments of familiarity. In the present study, we examined associative recognition ROCs for facial stimuli by manipulating the central and external features, in order to determine whether linear ROCs would be observed for stimuli other than arbitrary word pairs. When the faces were presented upright, familiarity estimates were significantly above zero, and the associative ROCs were curvilinear, suggesting that familiarity contributed to associative judgments. However, presenting the faces upside down effectively eliminated the contribution of familiarity to associative recognition, and the ROCs were linear. The results suggest that familiarity can support associative recognition judgments, if the associated components are encoded as a coherent gestalt, as in upright faces.

  12. Learning Compact Binary Face Descriptor for Face Recognition.

    PubMed

    Lu, Jiwen; Liong, Venice Erin; Zhou, Xiuzhuang; Zhou, Jie

    2015-10-01

    Binary feature descriptors such as local binary patterns (LBP) and its variations have been widely used in many face recognition systems due to their excellent robustness and strong discriminative power. However, most existing binary face descriptors are hand-crafted, which require strong prior knowledge to engineer them by hand. In this paper, we propose a compact binary face descriptor (CBFD) feature learning method for face representation and recognition. Given each face image, we first extract pixel difference vectors (PDVs) in local patches by computing the difference between each pixel and its neighboring pixels. Then, we learn a feature mapping to project these pixel difference vectors into low-dimensional binary vectors in an unsupervised manner, where 1) the variance of all binary codes in the training set is maximized, 2) the loss between the original real-valued codes and the learned binary codes is minimized, and 3) binary codes evenly distribute at each learned bin, so that the redundancy information in PDVs is removed and compact binary codes are obtained. Lastly, we cluster and pool these binary codes into a histogram feature as the final representation for each face image. Moreover, we propose a coupled CBFD (C-CBFD) method by reducing the modality gap of heterogeneous faces at the feature level to make our method applicable to heterogeneous face recognition. Extensive experimental results on five widely used face datasets show that our methods outperform state-of-the-art face descriptors.

  13. Perspective projection for variance pose face recognition from camera calibration

    NASA Astrophysics Data System (ADS)

    Fakhir, M. M.; Woo, W. L.; Chambers, J. A.; Dlay, S. S.

    2016-04-01

    Variance pose is an important research topic in face recognition. The alteration of distance parameters across variance pose face features is a challenging. We provide a solution for this problem using perspective projection for variance pose face recognition. Our method infers intrinsic camera parameters of the image which enable the projection of the image plane into 3D. After this, face box tracking and centre of eyes detection can be identified using our novel technique to verify the virtual face feature measurements. The coordinate system of the perspective projection for face tracking allows the holistic dimensions for the face to be fixed in different orientations. The training of frontal images and the rest of the poses on FERET database determine the distance from the centre of eyes to the corner of box face. The recognition system compares the gallery of images against different poses. The system initially utilises information on position of both eyes then focuses principally on closest eye in order to gather data with greater reliability. Differentiation between the distances and position of the right and left eyes is a unique feature of our work with our algorithm outperforming other state of the art algorithms thus enabling stable measurement in variance pose for each individual.

  14. How Fast is Famous Face Recognition?

    PubMed Central

    Barragan-Jason, Gladys; Lachat, Fanny; Barbeau, Emmanuel J.

    2012-01-01

    The rapid recognition of familiar faces is crucial for social interactions. However the actual speed with which recognition can be achieved remains largely unknown as most studies have been carried out without any speed constraints. Different paradigms have been used, leading to conflicting results, and although many authors suggest that face recognition is fast, the speed of face recognition has not been directly compared to “fast” visual tasks. In this study, we sought to overcome these limitations. Subjects performed three tasks, a familiarity categorization task (famous faces among unknown faces), a superordinate categorization task (human faces among animal ones), and a gender categorization task. All tasks were performed under speed constraints. The results show that, despite the use of speed constraints, subjects were slow when they had to categorize famous faces: minimum reaction time was 467 ms, which is 180 ms more than during superordinate categorization and 160 ms more than in the gender condition. Our results are compatible with a hierarchy of face processing from the superordinate level to the familiarity level. The processes taking place between detection and recognition need to be investigated in detail. PMID:23162503

  15. Proposal for the development of 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)

    SciTech Connect

    Deptuch, Gregory; Hoff, Jim; Kwan, Simon; Lipton, Ron; Liu, Ted; Ramberg, Erik; Todri, Aida; Yarema, Ray; Demarteua, Marcel,; Drake, Gary; Weerts, Harry; /Argonne /Chicago U. /Padua U. /INFN, Padua

    2010-10-01

    Future particle physics experiments looking for rare processes will have no choice but to address the demanding challenges of fast pattern recognition in triggering as detector hit density becomes significantly higher due to the high luminosity required to produce the rare process. The authors propose to develop a 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM) chip for HEP applications, to advance the state-of-the-art for pattern recognition and track reconstruction for fast triggering.

  16. 3-D Object Recognition Using Combined Overhead And Robot Eye-In-Hand Vision System

    NASA Astrophysics Data System (ADS)

    Luc, Ren C.; Lin, Min-Hsiung

    1987-10-01

    A new approach for recognizing 3-D objects using a combined overhead and eye-in-hand vision system is presented. A novel eye-in-hand vision system using a fiber-optic image array is described. The significance of this approach is the fast and accurate recognition of 3-D object information compared to traditional stereo image processing. For the recognition of 3-D objects, the over-head vision system will take 2-D top view image and the eye-in-hand vision system will take side view images orthogonal to the top view image plane. We have developed and demonstrated a unique approach to integrate this 2-D information into a 3-D representation based on a new approach called "3-D Volumetric Descrip-tion from 2-D Orthogonal Projections". The Unimate PUMA 560 and TRAPIX 5500 real-time image processor have been used to test the success of the entire system.

  17. [Face recognition in patients with schizophrenia].

    PubMed

    Doi, Hirokazu; Shinohara, Kazuyuki

    2012-07-01

    It is well known that patients with schizophrenia show severe deficiencies in social communication skills. These deficiencies are believed to be partly derived from abnormalities in face recognition. However, the exact nature of these abnormalities exhibited by schizophrenic patients with respect to face recognition has yet to be clarified. In the present paper, we review the main findings on face recognition deficiencies in patients with schizophrenia, particularly focusing on abnormalities in the recognition of facial expression and gaze direction, which are the primary sources of information of others' mental states. The existing studies reveal that the abnormal recognition of facial expression and gaze direction in schizophrenic patients is attributable to impairments in both perceptual processing of visual stimuli, and cognitive-emotional responses to social information. Furthermore, schizophrenic patients show malfunctions in distributed neural regions, ranging from the fusiform gyrus recruited in the structural encoding of facial stimuli, to the amygdala which plays a primary role in the detection of the emotional significance of stimuli. These findings were obtained from research in patient groups with heterogeneous characteristics. Because previous studies have indicated that impairments in face recognition in schizophrenic patients might vary according to the types of symptoms, it is of primary importance to compare the nature of face recognition deficiencies and the impairments of underlying neural functions across sub-groups of patients.

  18. Dense 3D Face Alignment from 2D Videos in Real-Time

    PubMed Central

    Jeni, László A.; Cohn, Jeffrey F.; Kanade, Takeo

    2016-01-01

    To enable real-time, person-independent 3D registration from 2D video, we developed a 3D cascade regression approach in which facial landmarks remain invariant across pose over a range of approximately 60 degrees. From a single 2D image of a person's face, a dense 3D shape is registered in real time for each frame. The algorithm utilizes a fast cascade regression framework trained on high-resolution 3D face-scans of posed and spontaneous emotion expression. The algorithm first estimates the location of a dense set of markers and their visibility, then reconstructs face shapes by fitting a part-based 3D model. Because no assumptions are required about illumination or surface properties, the method can be applied to a wide range of imaging conditions that include 2D video and uncalibrated multi-view video. The method has been validated in a battery of experiments that evaluate its precision of 3D reconstruction and extension to multi-view reconstruction. Experimental findings strongly support the validity of real-time, 3D registration and reconstruction from 2D video. The software is available online at http://zface.org. PMID:27293385

  19. Robust 3D face landmark localization based on local coordinate coding.

    PubMed

    Song, Mingli; Tao, Dacheng; Sun, Shengpeng; Chen, Chun; Maybank, Stephen J

    2014-12-01

    In the 3D facial animation and synthesis community, input faces are usually required to be labeled by a set of landmarks for parameterization. Because of the variations in pose, expression and resolution, automatic 3D face landmark localization remains a challenge. In this paper, a novel landmark localization approach is presented. The approach is based on local coordinate coding (LCC) and consists of two stages. In the first stage, we perform nose detection, relying on the fact that the nose shape is usually invariant under the variations in the pose, expression, and resolution. Then, we use the iterative closest points algorithm to find a 3D affine transformation that aligns the input face to a reference face. In the second stage, we perform resampling to build correspondences between the input 3D face and the training faces. Then, an LCC-based localization algorithm is proposed to obtain the positions of the landmarks in the input face. Experimental results show that the proposed method is comparable to state of the art methods in terms of its robustness, flexibility, and accuracy.

  20. Facial recognition software success rates for the identification of 3D surface reconstructed facial images: implications for patient privacy and security.

    PubMed

    Mazura, Jan C; Juluru, Krishna; Chen, Joseph J; Morgan, Tara A; John, Majnu; Siegel, Eliot L

    2012-06-01

    Image de-identification has focused on the removal of textual protected health information (PHI). Surface reconstructions of the face have the potential to reveal a subject's identity even when textual PHI is absent. This study assessed the ability of a computer application to match research subjects' 3D facial reconstructions with conventional photographs of their face. In a prospective study, 29 subjects underwent CT scans of the head and had frontal digital photographs of their face taken. Facial reconstructions of each CT dataset were generated on a 3D workstation. In phase 1, photographs of the 29 subjects undergoing CT scans were added to a digital directory and tested for recognition using facial recognition software. In phases 2-4, additional photographs were added in groups of 50 to increase the pool of possible matches and the test for recognition was repeated. As an internal control, photographs of all subjects were tested for recognition against an identical photograph. Of 3D reconstructions, 27.5% were matched correctly to corresponding photographs (95% upper CL, 40.1%). All study subject photographs were matched correctly to identical photographs (95% lower CL, 88.6%). Of 3D reconstructions, 96.6% were recognized simply as a face by the software (95% lower CL, 83.5%). Facial recognition software has the potential to recognize features on 3D CT surface reconstructions and match these with photographs, with implications for PHI.

  1. A novel thermal face recognition approach using face pattern words

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng

    2010-04-01

    A reliable thermal face recognition system can enhance the national security applications such as prevention against terrorism, surveillance, monitoring and tracking, especially at nighttime. The system can be applied at airports, customs or high-alert facilities (e.g., nuclear power plant) for 24 hours a day. In this paper, we propose a novel face recognition approach utilizing thermal (long wave infrared) face images that can automatically identify a subject at both daytime and nighttime. With a properly acquired thermal image (as a query image) in monitoring zone, the following processes will be employed: normalization and denoising, face detection, face alignment, face masking, Gabor wavelet transform, face pattern words (FPWs) creation, face identification by similarity measure (Hamming distance). If eyeglasses are present on a subject's face, an eyeglasses mask will be automatically extracted from the querying face image, and then masked with all comparing FPWs (no more transforms). A high identification rate (97.44% with Top-1 match) has been achieved upon our preliminary face dataset (of 39 subjects) from the proposed approach regardless operating time and glasses-wearing condition.e

  2. TDSIFT: a new descriptor for 2D and 3D ear recognition

    NASA Astrophysics Data System (ADS)

    Chen, Long; Mu, Zhichun; Nan, Bingfei; Zhang, Yi; Yang, Ruyin

    2017-02-01

    Descriptor is the key of any image-based recognition algorithm. For ear recognition, conventional descriptors are either based on 2D data or 3D data. 2D images provide rich texture information and human ear is a 3D surface that could offer shape information. It also inspires us that 2D data is more robust against occlusion while 3D data shows more robustness against illumination variation and pose variation. In this paper, we introduce a novel Texture and Depth Scale Invariant Feature Transform (TDSIFT) descriptor to encode 2D and 3D local features for ear recognition. Compared to the original Scale Invariant Feature Transform (SIFT) descriptor, the proposed TDSIFT shows its superiority by fusing 2D local information and 3D local information. Firstly, keypoints are detected and described on texture images. Then, 3D information of the keypoints located on the corresponding depth images is added to form the TDSIFT descriptor. Finally, a local feature based classification algorithm is adopted to identify ear samples by TDSIFT. Experimental results on a benchmark dataset demonstrate the feasibility and effectiveness of our proposed descriptor. The rank-1 recognition rate achieved on a gallery of 415 persons is 95.9% and the time involved in the computation is satisfactory compared to state-of-the-art methods.

  3. Face Recognition With Neural Networks

    DTIC Science & Technology

    1992-12-01

    condition known as prosopagnosia . Both researchers agree that patients with prosopagnosia , when they have come to autopsy, always have bilateral lesions...parietal region) do not have prosopagnosia . This also supports, albeit in a limited manner, the notion that the process is localized. Accepting...global to local idea is also supported in the prosopagnosia studies. Individuals with prosopagnosia can still identify a face as a face, but they can

  4. Higher-Order Neural Networks Applied to 2D and 3D Object Recognition

    NASA Technical Reports Server (NTRS)

    Spirkovska, Lilly; Reid, Max B.

    1994-01-01

    A Higher-Order Neural Network (HONN) can be designed to be invariant to geometric transformations such as scale, translation, and in-plane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Thus, for 2D object recognition, the network needs to be trained on just one view of each object class, not numerous scaled, translated, and rotated views. Because the 2D object recognition task is a component of the 3D object recognition task, built-in 2D invariance also decreases the size of the training set required for 3D object recognition. We present results for 2D object recognition both in simulation and within a robotic vision experiment and for 3D object recognition in simulation. We also compare our method to other approaches and show that HONNs have distinct advantages for position, scale, and rotation-invariant object recognition. The major drawback of HONNs is that the size of the input field is limited due to the memory required for the large number of interconnections in a fully connected network. We present partial connectivity strategies and a coarse-coding technique for overcoming this limitation and increasing the input field to that required by practical object recognition problems.

  5. Efficient live face detection to counter spoof attack in face recognition systems

    NASA Astrophysics Data System (ADS)

    Biswas, Bikram Kumar; Alam, Mohammad S.

    2015-03-01

    Face recognition is a critical tool used in almost all major biometrics based security systems. But recognition, authentication and liveness detection of the face of an actual user is a major challenge because an imposter or a non-live face of the actual user can be used to spoof the security system. In this research, a robust technique is proposed which detects liveness of faces in order to counter spoof attacks. The proposed technique uses a three-dimensional (3D) fast Fourier transform to compare spectral energies of a live face and a fake face in a mathematically selective manner. The mathematical model involves evaluation of energies of selective high frequency bands of average power spectra of both live and non-live faces. It also carries out proper recognition and authentication of the face of the actual user using the fringe-adjusted joint transform correlation technique, which has been found to yield the highest correlation output for a match. Experimental tests show that the proposed technique yields excellent results for identifying live faces.

  6. Maximal likelihood correspondence estimation for face recognition across pose.

    PubMed

    Li, Shaoxin; Liu, Xin; Chai, Xiujuan; Zhang, Haihong; Lao, Shihong; Shan, Shiguang

    2014-10-01

    Due to the misalignment of image features, the performance of many conventional face recognition methods degrades considerably in across pose scenario. To address this problem, many image matching-based methods are proposed to estimate semantic correspondence between faces in different poses. In this paper, we aim to solve two critical problems in previous image matching-based correspondence learning methods: 1) fail to fully exploit face specific structure information in correspondence estimation and 2) fail to learn personalized correspondence for each probe image. To this end, we first build a model, termed as morphable displacement field (MDF), to encode face specific structure information of semantic correspondence from a set of real samples of correspondences calculated from 3D face models. Then, we propose a maximal likelihood correspondence estimation (MLCE) method to learn personalized correspondence based on maximal likelihood frontal face assumption. After obtaining the semantic correspondence encoded in the learned displacement, we can synthesize virtual frontal images of the profile faces for subsequent recognition. Using linear discriminant analysis method with pixel-intensity features, state-of-the-art performance is achieved on three multipose benchmarks, i.e., CMU-PIE, FERET, and MultiPIE databases. Owe to the rational MDF regularization and the usage of novel maximal likelihood objective, the proposed MLCE method can reliably learn correspondence between faces in different poses even in complex wild environment, i.e., labeled face in the wild database.

  7. Voice Recognition in Face-Blind Patients

    PubMed Central

    Liu, Ran R.; Pancaroglu, Raika; Hills, Charlotte S.; Duchaine, Brad; Barton, Jason J. S.

    2016-01-01

    Right or bilateral anterior temporal damage can impair face recognition, but whether this is an associative variant of prosopagnosia or part of a multimodal disorder of person recognition is an unsettled question, with implications for cognitive and neuroanatomic models of person recognition. We assessed voice perception and short-term recognition of recently heard voices in 10 subjects with impaired face recognition acquired after cerebral lesions. All 4 subjects with apperceptive prosopagnosia due to lesions limited to fusiform cortex had intact voice discrimination and recognition. One subject with bilateral fusiform and anterior temporal lesions had a combined apperceptive prosopagnosia and apperceptive phonagnosia, the first such described case. Deficits indicating a multimodal syndrome of person recognition were found only in 2 subjects with bilateral anterior temporal lesions. All 3 subjects with right anterior temporal lesions had normal voice perception and recognition, 2 of whom performed normally on perceptual discrimination of faces. This confirms that such lesions can cause a modality-specific associative prosopagnosia. PMID:25349193

  8. Face detection and eyeglasses detection for thermal face recognition

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng

    2012-01-01

    Thermal face recognition becomes an active research direction in human identification because it does not rely on illumination condition. Face detection and eyeglasses detection are necessary steps prior to face recognition using thermal images. Infrared light cannot go through glasses and thus glasses will appear as dark areas in a thermal image. One possible solution is to detect eyeglasses and to exclude the eyeglasses areas before face matching. In thermal face detection, a projection profile analysis algorithm is proposed, where region growing and morphology operations are used to segment the body of a subject; then the derivatives of two projections (horizontal and vertical) are calculated and analyzed to locate a minimal rectangle of containing the face area. Of course, the searching region of a pair of eyeglasses is within the detected face area. The eyeglasses detection algorithm should produce either a binary mask if eyeglasses present, or an empty set if no eyeglasses at all. In the proposed eyeglasses detection algorithm, block processing, region growing, and priori knowledge (i.e., low mean and variance within glasses areas, the shapes and locations of eyeglasses) are employed. The results of face detection and eyeglasses detection are quantitatively measured and analyzed using the manually defined ground truths (for both face and eyeglasses). Our experimental results shown that the proposed face detection and eyeglasses detection algorithms performed very well in contrast with the predefined ground truths.

  9. Embedded wavelet-based face recognition under variable position

    NASA Astrophysics Data System (ADS)

    Cotret, Pascal; Chevobbe, Stéphane; Darouich, Mehdi

    2015-02-01

    For several years, face recognition has been a hot topic in the image processing field: this technique is applied in several domains such as CCTV, electronic devices delocking and so on. In this context, this work studies the efficiency of a wavelet-based face recognition method in terms of subject position robustness and performance on various systems. The use of wavelet transform has a limited impact on the position robustness of PCA-based face recognition. This work shows, for a well-known database (Yale face database B*), that subject position in a 3D space can vary up to 10% of the original ROI size without decreasing recognition rates. Face recognition is performed on approximation coefficients of the image wavelet transform: results are still satisfying after 3 levels of decomposition. Furthermore, face database size can be divided by a factor 64 (22K with K = 3). In the context of ultra-embedded vision systems, memory footprint is one of the key points to be addressed; that is the reason why compression techniques such as wavelet transform are interesting. Furthermore, it leads to a low-complexity face detection stage compliant with limited computation resources available on such systems. The approach described in this work is tested on three platforms from a standard x86-based computer towards nanocomputers such as RaspberryPi and SECO boards. For K = 3 and a database with 40 faces, the execution mean time for one frame is 0.64 ms on a x86-based computer, 9 ms on a SECO board and 26 ms on a RaspberryPi (B model).

  10. Face-space: A unifying concept in face recognition research.

    PubMed

    Valentine, Tim; Lewis, Michael B; Hills, Peter J

    2016-10-01

    The concept of a multidimensional psychological space, in which faces can be represented according to their perceived properties, is fundamental to the modern theorist in face processing. Yet the idea was not clearly expressed until 1991. The background that led to the development of face-space is explained, and its continuing influence on theories of face processing is discussed. Research that has explored the properties of the face-space and sought to understand caricature, including facial adaptation paradigms, is reviewed. Face-space as a theoretical framework for understanding the effect of ethnicity and the development of face recognition is evaluated. Finally, two applications of face-space in the forensic setting are discussed. From initially being presented as a model to explain distinctiveness, inversion, and the effect of ethnicity, face-space has become a central pillar in many aspects of face processing. It is currently being developed to help us understand adaptation effects with faces. While being in principle a simple concept, face-space has shaped, and continues to shape, our understanding of face perception.

  11. Interactive Cosmetic Makeup of a 3D Point-Based Face Model

    NASA Astrophysics Data System (ADS)

    Kim, Jeong-Sik; Choi, Soo-Mi

    We present an interactive system for cosmetic makeup of a point-based face model acquired by 3D scanners. We first enhance the texture of a face model in 3D space using low-pass Gaussian filtering, median filtering, and histogram equalization. The user is provided with a stereoscopic display and haptic feedback, and can perform simulated makeup tasks including the application of foundation, color makeup, and lip gloss. Fast rendering is achieved by processing surfels using the GPU, and we use a BSP tree data structure and a dynamic local refinement of the facial surface to provide interactive haptics. We have implemented a prototype system and evaluated its performance.

  12. Multi-Modality Vertebra Recognition in Arbitrary Views Using 3D Deformable Hierarchical Model.

    PubMed

    Cai, Yunliang; Osman, Said; Sharma, Manas; Landis, Mark; Li, Shuo

    2015-08-01

    Computer-aided diagnosis of spine problems relies on the automatic identification of spine structures in images. The task of automatic vertebra recognition is to identify the global spine and local vertebra structural information such as spine shape, vertebra location and pose. Vertebra recognition is challenging due to the large appearance variations in different image modalities/views and the high geometric distortions in spine shape. Existing vertebra recognitions are usually simplified as vertebrae detections, which mainly focuses on the identification of vertebra locations and labels but cannot support further spine quantitative assessment. In this paper, we propose a vertebra recognition method using 3D deformable hierarchical model (DHM) to achieve cross-modality local vertebra location+pose identification with accurate vertebra labeling, and global 3D spine shape recovery. We recast vertebra recognition as deformable model matching, fitting the input spine images with the 3D DHM via deformations. The 3D model-matching mechanism provides a more comprehensive vertebra location+pose+label simultaneous identification than traditional vertebra location+label detection, and also provides an articulated 3D mesh model for the input spine section. Moreover, DHM can conduct versatile recognition on volume and multi-slice data, even on single slice. Experiments show our method can successfully extract vertebra locations, labels, and poses from multi-slice T1/T2 MR and volume CT, and can reconstruct 3D spine model on different image views such as lumbar, cervical, even whole spine. The resulting vertebra information and the recovered shape can be used for quantitative diagnosis of spine problems and can be easily digitalized and integrated in modern medical PACS systems.

  13. About-face on face recognition ability and holistic processing

    PubMed Central

    Richler, Jennifer J.; Floyd, R. Jackie; Gauthier, Isabel

    2015-01-01

    Previous work found a small but significant relationship between holistic processing measured with the composite task and face recognition ability measured by the Cambridge Face Memory Test (CFMT; Duchaine & Nakayama, 2006). Surprisingly, recent work using a different measure of holistic processing (Vanderbilt Holistic Face Processing Test [VHPT-F]; Richler, Floyd, & Gauthier, 2014) and a larger sample found no evidence for such a relationship. In Experiment 1 we replicate this unexpected result, finding no relationship between holistic processing (VHPT-F) and face recognition ability (CFMT). A key difference between the VHPT-F and other holistic processing measures is that unique face parts are used on each trial in the VHPT-F, unlike in other tasks where a small set of face parts repeat across the experiment. In Experiment 2, we test the hypothesis that correlations between the CFMT and holistic processing tasks are driven by stimulus repetition that allows for learning during the composite task. Consistent with our predictions, CFMT performance was correlated with holistic processing in the composite task when a small set of face parts repeated over trials, but not when face parts did not repeat. A meta-analysis confirms that relationships between the CFMT and holistic processing depend on stimulus repetition. These results raise important questions about what is being measured by the CFMT, and challenge current assumptions about why faces are processed holistically. PMID:26223027

  14. Robust kernel collaborative representation for face recognition

    NASA Astrophysics Data System (ADS)

    Huang, Wei; Wang, Xiaohui; Ma, Yanbo; Jiang, Yuzheng; Zhu, Yinghui; Jin, Zhong

    2015-05-01

    One of the greatest challenges of representation-based face recognition is that the training samples are usually insufficient. In other words, the training set usually does not include enough samples to show varieties of high-dimensional face images caused by illuminations, facial expressions, and postures. When the test sample is significantly different from the training samples of the same subject, the recognition performance will be sharply reduced. We propose a robust kernel collaborative representation based on virtual samples for face recognition. We think that the virtual training set conveys some reasonable and possible variations of the original training samples. Hence, we design a new object function to more closely match the representation coefficients generated from the original and virtual training sets. In order to further improve the robustness, we implement the corresponding representation-based face recognition in kernel space. It is noteworthy that any kind of virtual training samples can be used in our method. We use noised face images to obtain virtual face samples. The noise can be approximately viewed as a reflection of the varieties of illuminations, facial expressions, and postures. Our work is a simple and feasible way to obtain virtual face samples to impose Gaussian noise (and other types of noise) specifically to the original training samples to obtain possible variations of the original samples. Experimental results on the FERET, Georgia Tech, and ORL face databases show that the proposed method is more robust than two state-of-the-art face recognition methods, such as CRC and Kernel CRC.

  15. Self-face recognition in social context.

    PubMed

    Sugiura, Motoaki; Sassa, Yuko; Jeong, Hyeonjeong; Wakusawa, Keisuke; Horie, Kaoru; Sato, Shigeru; Kawashima, Ryuta

    2012-06-01

    The concept of "social self" is often described as a representation of the self-reflected in the eyes or minds of others. Although the appearance of one's own face has substantial social significance for humans, neuroimaging studies have failed to link self-face recognition and the likely neural substrate of the social self, the medial prefrontal cortex (MPFC). We assumed that the social self is recruited during self-face recognition under a rich social context where multiple other faces are available for comparison of social values. Using functional magnetic resonance imaging (fMRI), we examined the modulation of neural responses to the faces of the self and of a close friend in a social context. We identified an enhanced response in the ventral MPFC and right occipitoparietal sulcus in the social context specifically for the self-face. Neural response in the right lateral parietal and inferior temporal cortices, previously claimed as self-face-specific, was unaffected for the self-face but unexpectedly enhanced for the friend's face in the social context. Self-face-specific activation in the pars triangularis of the inferior frontal gyrus, and self-face-specific reduction of activation in the left middle temporal gyrus and the right supramarginal gyrus, replicating a previous finding, were not subject to such modulation. Our results thus demonstrated the recruitment of a social self during self-face recognition in the social context. At least three brain networks for self-face-specific activation may be dissociated by different patterns of response-modulation in the social context, suggesting multiple dynamic self-other representations in the human brain.

  16. Fast and reliable active appearance model search for 3-D face tracking.

    PubMed

    Dornaika, F; Ahlberg, J

    2004-08-01

    This paper addresses the three-dimensional (3-D) tracking of pose and animation of the human face in monocular image sequences using active appearance models. The major problem of the classical appearance-based adaptation is the high computational time resulting from the inclusion of a synthesis step in the iterative optimization. Whenever the dimension of the face space is large, a real-time performance cannot be achieved. In this paper, we aim at designing a fast and stable active appearance model search for 3-D face tracking. The main contribution is a search algorithm whose CPU-time is not dependent on the dimension of the face space. Using this algorithm, we show that both the CPU-time and the likelihood of a nonaccurate tracking are reduced. Experiments evaluating the effectiveness of the proposed algorithm are reported, as well as method comparison and tracking synthetic and real image sequences.

  17. Video face recognition against a watch list

    NASA Astrophysics Data System (ADS)

    Abbas, Jehanzeb; Dagli, Charlie K.; Huang, Thomas S.

    2007-10-01

    Due to a large increase in the video surveillance data recently in an effort to maintain high security at public places, we need more robust systems to analyze this data and make tasks like face recognition a realistic possibility in challenging environments. In this paper we explore a watch-list scenario where we use an appearance based model to classify query faces from low resolution videos into either a watch-list or a non-watch-list face. We then use our simple yet a powerful face recognition system to recognize the faces classified as watch-list faces. Where the watch-list includes those people that we are interested in recognizing. Our system uses simple feature machine algorithms from our previous work to match video faces against still images. To test our approach, we match video faces against a large database of still images obtained from a previous work in the field from Yahoo News over a period of time. We do this matching in an efficient manner to come up with a faster and nearly real-time system. This system can be incorporated into a larger surveillance system equipped with advanced algorithms involving anomalous event detection and activity recognition. This is a step towards more secure and robust surveillance systems and efficient video data analysis.

  18. Multi-view indoor human behavior recognition based on 3D skeleton

    NASA Astrophysics Data System (ADS)

    Peng, Ling; Lu, Tongwei; Min, Feng

    2015-12-01

    For the problems caused by viewpoint changes in activity recognition, a multi-view interior human behavior recognition method based on 3D framework is presented. First, Microsoft's Kinect device is used to obtain body motion video in the positive perspective, the oblique angle and the side perspective. Second, it extracts bone joints and get global human features and the local features of arms and legs at the same time to form 3D skeletal features set. Third, online dictionary learning on feature set is used to reduce the dimension of feature. Finally, linear support vector machine (LSVM) is used to obtain the results of behavior recognition. The experimental results show that this method has better recognition rate.

  19. Face Recognition Using Infrared Imaging

    DTIC Science & Technology

    2002-12-01

    the adjacent skin . The typical human face displays an apparent temperature range of about 8oC. The complexity of the resulting 3.1 miles (five...have shown that even identical twins have different thermogram features [16]. Furthermore, studies have shown that the skin temperature ex- 8 hibits...Ishigaki, K. Mabuchi, ”Thermal Rhytmography- Topograms of the Spectral Analysis of Fluctuations in Skin Temperature ,” Proceedings of the 23rd Annual

  20. Compositional Dictionaries for Domain Adaptive Face Recognition.

    PubMed

    Qiang Qiu; Chellappa, Rama

    2015-12-01

    We present a dictionary learning approach to compensate for the transformation of faces due to the changes in view point, illumination, resolution, and so on. The key idea of our approach is to force domain-invariant sparse coding, i.e., designing a consistent sparse representation of the same face in different domains. In this way, the classifiers trained on the sparse codes in the source domain consisting of frontal faces can be applied to the target domain (consisting of faces in different poses, illumination conditions, and so on) without much loss in recognition accuracy. The approach is to first learn a domain base dictionary, and then describe each domain shift (identity, pose, and illumination) using a sparse representation over the base dictionary. The dictionary adapted to each domain is expressed as the sparse linear combinations of the base dictionary. In the context of face recognition, with the proposed compositional dictionary approach, a face image can be decomposed into sparse representations for a given subject, pose, and illumination. This approach has three advantages. First, the extracted sparse representation for a subject is consistent across domains, and enables pose and illumination insensitive face recognition. Second, sparse representations for pose and illumination can be subsequently used to estimate the pose and illumination condition of a face image. Last, by composing sparse representations for the subject and the different domains, we can also perform pose alignment and illumination normalization. Extensive experiments using two public face data sets are presented to demonstrate the effectiveness of the proposed approach for face recognition.

  1. Workbench for 3D target detection and recognition from airborne motion stereo and ladar imagery

    NASA Astrophysics Data System (ADS)

    Roy, Simon; Se, Stephen; Kotamraju, Vinay; Maheux, Jean; Nadeau, Christian; Larochelle, Vincent; Fournier, Jonathan

    2010-04-01

    3D imagery has a well-known potential for improving situational awareness and battlespace visualization by providing enhanced knowledge of uncooperative targets. This potential arises from the numerous advantages that 3D imagery has to offer over traditional 2D imagery, thereby increasing the accuracy of automatic target detection (ATD) and recognition (ATR). Despite advancements in both 3D sensing and 3D data exploitation, 3D imagery has yet to demonstrate a true operational gain, partly due to the processing burden of the massive dataloads generated by modern sensors. In this context, this paper describes the current status of a workbench designed for the study of 3D ATD/ATR. Among the project goals is the comparative assessment of algorithms and 3D sensing technologies given various scenarios. The workbench is comprised of three components: a database, a toolbox, and a simulation environment. The database stores, manages, and edits input data of various types such as point clouds, video, still imagery frames, CAD models and metadata. The toolbox features data processing modules, including range data manipulation, surface mesh generation, texture mapping, and a shape-from-motion module to extract a 3D target representation from video frames or from a sequence of still imagery. The simulation environment includes synthetic point cloud generation, 3D ATD/ATR algorithm prototyping environment and performance metrics for comparative assessment. In this paper, the workbench components are described and preliminary results are presented. Ladar, video and still imagery datasets collected during airborne trials are also detailed.

  2. FaceID: A face detection and recognition system

    SciTech Connect

    Shah, M.B.; Rao, N.S.V.; Olman, V.; Uberbacher, E.C.; Mann, R.C.

    1996-12-31

    A face detection system that automatically locates faces in gray-level images is described. Also described is a system which matches a given face image with faces in a database. Face detection in an Image is performed by template matching using templates derived from a selected set of normalized faces. Instead of using original gray level images, vertical gradient images were calculated and used to make the system more robust against variations in lighting conditions and skin color. Faces of different sizes are detected by processing the image at several scales. Further, a coarse-to-fine strategy is used to speed up the processing, and a combination of whole face and face component templates are used to ensure low false detection rates. The input to the face recognition system is a normalized vertical gradient image of a face, which is compared against a database using a set of pretrained feedforward neural networks with a winner-take-all fuser. The training is performed by using an adaptation of the backpropagation algorithm. This system has been developed and tested using images from the FERET database and a set of images obtained from Rowley, et al and Sung and Poggio.

  3. The Chinese Facial Emotion Recognition Database (CFERD): a computer-generated 3-D paradigm to measure the recognition of facial emotional expressions at different intensities.

    PubMed

    Huang, Charles Lung-Cheng; Hsiao, Sigmund; Hwu, Hai-Gwo; Howng, Shen-Long

    2012-12-30

    The Chinese Facial Emotion Recognition Database (CFERD), a computer-generated three-dimensional (3D) paradigm, was developed to measure the recognition of facial emotional expressions at different intensities. The stimuli consisted of 3D colour photographic images of six basic facial emotional expressions (happiness, sadness, disgust, fear, anger and surprise) and neutral faces of the Chinese. The purpose of the present study is to describe the development and validation of CFERD with nonclinical healthy participants (N=100; 50 men; age ranging between 18 and 50 years), and to generate normative data set. The results showed that the sensitivity index d' [d'=Z(hit rate)-Z(false alarm rate), where function Z(p), p∈[0,1

  4. Recognition of 3D objects for autonomous mobile robot's navigation in automated shipbuilding

    NASA Astrophysics Data System (ADS)

    Lee, Hyunki; Cho, Hyungsuck

    2007-10-01

    Nowadays many parts of shipbuilding process are automated, but the painting process is not, because of the difficulty of automated on-line painting quality measurement, harsh painting environment and the difficulty of robot navigation. However, the painting automation is necessary, because it can provide consistent performance of painting film thickness. Furthermore, autonomous mobile robots are strongly required for flexible painting work. However, the main problem of autonomous mobile robot's navigation is that there are many obstacles which are not expressed in the CAD data. To overcome this problem, obstacle detection and recognition are necessary to avoid obstacles and painting work effectively. Until now many object recognition algorithms have been studied, especially 2D object recognition methods using intensity image have been widely studied. However, in our case environmental illumination does not exist, so these methods cannot be used. To overcome this, to use 3D range data must be used, but the problem of using 3D range data is high computational cost and long estimation time of recognition due to huge data base. In this paper, we propose a 3D object recognition algorithm based on PCA (Principle Component Analysis) and NN (Neural Network). In the algorithm, the novelty is that the measured 3D range data is transformed into intensity information, and then adopts the PCA and NN algorithm for transformed intensity information to reduce the processing time and make the data easy to handle which are disadvantages of previous researches of 3D object recognition. A set of experimental results are shown to verify the effectiveness of the proposed algorithm.

  5. Pseudo-Gabor wavelet for face recognition

    NASA Astrophysics Data System (ADS)

    Xie, Xudong; Liu, Wentao; Lam, Kin-Man

    2013-04-01

    An efficient face-recognition algorithm is proposed, which not only possesses the advantages of linear subspace analysis approaches-such as low computational complexity-but also has the advantage of a high recognition performance with the wavelet-based algorithms. Based on the linearity of Gabor-wavelet transformation and some basic assumptions on face images, we can extract pseudo-Gabor features from the face images without performing any complex Gabor-wavelet transformations. The computational complexity can therefore be reduced while a high recognition performance is still maintained by using the principal component analysis (PCA) method. The proposed algorithm is evaluated based on the Yale database, the Caltech database, the ORL database, the AR database, and the Facial Recognition Technology database, and is compared with several different face recognition methods such as PCA, Gabor wavelets plus PCA, kernel PCA, locality preserving projection, and dual-tree complex wavelet transformation plus PCA. Experiments show that consistent and promising results are obtained.

  6. Influence of motion on face recognition.

    PubMed

    Bonfiglio, Natale S; Manfredi, Valentina; Pessa, Eliano

    2012-02-01

    The influence of motion information and temporal associations on recognition of non-familiar faces was investigated using two groups which performed a face recognition task. One group was presented with regular temporal sequences of face views designed to produce the impression of motion of the face rotating in depth, the other group with random sequences of the same views. In one condition, participants viewed the sequences of the views in rapid succession with a negligible interstimulus interval (ISI). This condition was characterized by three different presentation times. In another condition, participants were presented a sequence with a 1-sec. ISI among the views. That regular sequences of views with a negligible ISI and a shorter presentation time were hypothesized to give rise to better recognition, related to a stronger impression of face rotation. Analysis of data from 45 participants showed a shorter presentation time was associated with significantly better accuracy on the recognition task; however, differences between performances associated with regular and random sequences were not significant.

  7. Wavelet transform application in human face recognition

    NASA Astrophysics Data System (ADS)

    Meng, Qiang; Thompson, Wiley E.; Flachs, Gerald M.; Jordan, Jay B.

    1997-07-01

    A wavelet transformation is introduced as a new method to extract sideview face features in human face recognition. Utilizing the wavelet transformation, a sideview profile is decomposed as high frequency and low frequency components. Signal reconstruction, autocorrelation and energy distribution are used to decide a optimal decomposition level in the wavelet transformation without losing sideview features. To evaluate the feasibility of the wavelet transformation features in human sideview face recognition, the tie statistic is used to compute the complexity of the wavelet transform features. Using wavelet transformation, the sideview data size is reduced. The reduced features have almost the same ability as the original sideview face profile data in terms of distinguishing different people. The computational expense is greatly decreased. The results of the experiments are also shown in this paper.

  8. Recognition of complex human behaviours using 3D imaging for intelligent surveillance applications

    NASA Astrophysics Data System (ADS)

    Yao, Bo; Lepley, Jason J.; Peall, Robert; Butler, Michael; Hagras, Hani

    2016-10-01

    We introduce a system that exploits 3-D imaging technology as an enabler for the robust recognition of the human form. We combine this with pose and feature recognition capabilities from which we can recognise high-level human behaviours. We propose a hierarchical methodology for the recognition of complex human behaviours, based on the identification of a set of atomic behaviours, individual and sequential poses (e.g. standing, sitting, walking, drinking and eating) that provides a framework from which we adopt time-based machine learning techniques to recognise complex behaviour patterns.

  9. Comparison of low cost 3D structured light scanners for face modeling.

    PubMed

    Bakirman, Tolga; Gumusay, Mustafa Umit; Reis, Hatice Catal; Selbesoglu, Mahmut Oguz; Yosmaoglu, Serra; Yaras, Mehmet Cem; Seker, Dursun Zafer; Bayram, Bulent

    2017-02-01

    This study aims to compare three different structured light scanner systems to generate accurate 3D human face models. Among these systems, the most dense and expensive one was denoted as the reference and the other two that were low cost and low resolution were compared according to the reference system. One female face and one male face were scanned with three light scanner systems. Point-cloud filtering, mesh generation, and hole-filling steps were carried out using a trial version of commercial software; moreover, the data evaluation process was realized using CloudCompare open-source software. Various filtering and mesh smoothing levels were applied on reference data to compare with other low-cost systems. Thus, the optimum reduction level of reference data was evaluated to continue further processes. The outcome of the presented study shows that low-cost structured light scanners have a great potential for 3D object modeling, including the human face. A considerable cheap structured light system has been used due to its capacity to obtain spatial and morphological information in the case study of 3D human face modeling. This study also discusses the benefits and accuracy of low-cost structured light systems.

  10. Metacognition of emotional face recognition.

    PubMed

    Kelly, Karen J; Metcalfe, Janet

    2011-08-01

    While humans are adept at recognizing emotional states conveyed by facial expressions, the current literature suggests that they lack accurate metacognitions about their performance in this domain. This finding comes from global trait-based questionnaires that assess the extent to which an individual perceives him or herself as empathic, as compared to other people. Those who rate themselves as empathically accurate are no better than others at recognizing emotions. Metacognition of emotion recognition can also be assessed using relative measures that evaluate how well a person thinks s/he has understood the emotion in a particular facial display as compared to other displays. While this is the most common method of metacognitive assessment of people's judgments of learning or their feelings of knowing, this kind of metacognition--"relative meta-accuracy"--has not been studied within the domain of emotion. As well as asking for global metacognitive judgments, we asked people to provide relative, trial-by-trial prospective and retrospective judgments concerning whether they would be right or wrong in recognizing the expressions conveyed in particular facial displays. Our question was: Do people know when they will be correct in knowing what expression is conveyed, and do they know when they do not know? Although we, like others, found that global meta-accuracy was unpredictive of performance, relative meta-accuracy, given by the correlation between participants' trial-by-trial metacognitive judgments and performance on each item, were highly accurate both on the Mind in the Eyes task (Experiment 1) and on the Ekman Emotional Expression Multimorph task (in Experiment 2). 2011 APA, all rights reserved

  11. A novel orientation code for face recognition

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng

    2011-06-01

    A novel orientation code is proposed for face recognition applications in this paper. Gabor wavelet transform is a common tool for orientation analysis in a 2D image; whereas Hamming distance is an efficient distance measurement for multiple classifications such as face identification. Specifically, at each frequency band, an index number representing the strongest orientational response is selected, and then encoded in binary format to favor the Hamming distance calculation. Multiple-band orientation codes are then organized into a face pattern byte (FPB) by using order statistics. With the FPB, Hamming distances are calculated and compared to achieve face identification. The FPB has the dimensionality of 8 bits per pixel and its performance will be compared to that of FPW (face pattern word, 32 bits per pixel). The dimensionality of FPB can be further reduced down to 4 bits per pixel, called face pattern nibble (FPN). Experimental results with visible and thermal face databases show that the proposed orientation code for face recognition is very promising in contrast with classical methods such as PCA.

  12. Face Recognition by Independent Component Analysis

    PubMed Central

    Bartlett, Marian Stewart; Movellan, Javier R.; Sejnowski, Terrence J.

    2010-01-01

    A number of current face recognition algorithms use face representations found by unsupervised statistical methods. Typically these methods find a set of basis images and represent faces as a linear combination of those images. Principal component analysis (PCA) is a popular example of such methods. The basis images found by PCA depend only on pairwise relationships between pixels in the image database. In a task such as face recognition, in which important information may be contained in the high-order relationships among pixels, it seems reasonable to expect that better basis images may be found by methods sensitive to these high-order statistics. Independent component analysis (ICA), a generalization of PCA, is one such method. We used a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons. ICA was performed on face images in the FERET database under two different architectures, one which treated the images as random variables and the pixels as outcomes, and a second which treated the pixels as random variables and the images as outcomes. The first architecture found spatially local basis images for the faces. The second architecture produced a factorial face code. Both ICA representations were superior to representations based on PCA for recognizing faces across days and changes in expression. A classifier that combined the two ICA representations gave the best performance. PMID:18244540

  13. Neurocomputational bases of object and face recognition.

    PubMed Central

    Biederman, I; Kalocsai, P

    1997-01-01

    A number of behavioural phenomena distinguish the recognition of faces and objects, even when members of a set of objects are highly similar. Because faces have the same parts in approximately the same relations, individuation of faces typically requires specification of the metric variation in a holistic and integral representation of the facial surface. The direct mapping of a hypercolumn-like pattern of activation onto a representation layer that preserves relative spatial filter values in a two-dimensional (2D) coordinate space, as proposed by C. von der Malsburg and his associates, may account for many of the phenomena associated with face recognition. An additional refinement, in which each column of filters (termed a 'jet') is centred on a particular facial feature (or fiducial point), allows selectivity of the input into the holistic representation to avoid incorporation of occluding or nearby surfaces. The initial hypercolumn representation also characterizes the first stage of object perception, but the image variation for objects at a given location in a 2D coordinate space may be too great to yield sufficient predictability directly from the output of spatial kernels. Consequently, objects can be represented by a structural description specifying qualitative (typically, non-accidental) characterizations of an object's parts, the attributes of the parts, and the relations among the parts, largely based on orientation and depth discontinuities (as shown by Hummel & Biederman). A series of experiments on the name priming or physical matching of complementary images (in the Fourier domain) of objects and faces documents that whereas face recognition is strongly dependent on the original spatial filter values, evidence from object recognition indicates strong invariance to these values, even when distinguishing among objects that are as similar as faces. PMID:9304687

  14. Wavelet-based multispectral face recognition

    NASA Astrophysics Data System (ADS)

    Liu, Dian-Ting; Zhou, Xiao-Dan; Wang, Cheng-Wen

    2008-09-01

    This paper proposes a novel wavelet-based face recognition method using thermal infrared (IR) and visible-light face images. The method applies the combination of Gabor and the Fisherfaces method to the reconstructed IR and visible images derived from wavelet frequency subbands. Our objective is to search for the subbands that are insensitive to the variation in expression and in illumination. The classification performance is improved by combining the multispectal information coming from the subbands that attain individually low equal error rate. Experimental results on Notre Dame face database show that the proposed wavelet-based algorithm outperforms previous multispectral images fusion method as well as monospectral method.

  15. Deep learning and face recognition: the state of the art

    NASA Astrophysics Data System (ADS)

    Balaban, Stephen

    2015-05-01

    Deep Neural Networks (DNNs) have established themselves as a dominant technique in machine learning. DNNs have been top performers on a wide variety of tasks including image classification, speech recognition, and face recognition.1-3 Convolutional neural networks (CNNs) have been used in nearly all of the top performing methods on the Labeled Faces in the Wild (LFW) dataset.3-6 In this talk and accompanying paper, I attempt to provide a review and summary of the deep learning techniques used in the state-of-the-art. In addition, I highlight the need for both larger and more challenging public datasets to benchmark these systems. Despite the ability of DNNs and autoencoders to perform unsupervised feature learning, modern facial recognition pipelines still require domain specific engineering in the form of re-alignment. For example, in Facebook's recent DeepFace paper, a 3D "frontalization" step lies at the beginning of the pipeline. This step creates a 3D face model for the incoming image and then uses a series of affine transformations of the fiducial points to "frontalize" the image. This step enables the DeepFace system to use a neural network architecture with locally connected layers without weight sharing as opposed to standard convolutional layers.6 Deep learning techniques combined with large datasets have allowed research groups to surpass human level performance on the LFW dataset.3, 5 The high accuracy (99.63% for FaceNet at the time of publishing) and utilization of outside data (hundreds of millions of images in the case of Google's FaceNet) suggest that current face verification benchmarks such as LFW may not be challenging enough, nor provide enough data, for current techniques.3, 5 There exist a variety of organizations with mobile photo sharing applications that would be capable of releasing a very large scale and highly diverse dataset of facial images captured on mobile devices. Such an "ImageNet for Face Recognition" would likely receive a warm

  16. A Two-Stage Framework for 3D Face Reconstruction from RGBD Images.

    PubMed

    Wang, Kangkan; Wang, Xianwang; Pan, Zhigeng; Liu, Kai

    2014-08-01

    This paper proposes a new approach for 3D face reconstruction with RGBD images from an inexpensive commodity sensor. The challenges we face are: 1) substantial random noise and corruption are present in low-resolution depth maps; and 2) there is high degree of variability in pose and face expression. We develop a novel two-stage algorithm that effectively maps low-quality depth maps to realistic face models. Each stage is targeted toward a certain type of noise. The first stage extracts sparse errors from depth patches through the data-driven local sparse coding, while the second stage smooths noise on the boundaries between patches and reconstructs the global shape by combining local shapes using our template-based surface refinement. Our approach does not require any markers or user interaction. We perform quantitative and qualitative evaluations on both synthetic and real test sets. Experimental results show that the proposed approach is able to produce high-resolution 3D face models with high accuracy, even if inputs are of low quality, and have large variations in viewpoint and face expression.

  17. Serotonergic modulation of face-emotion recognition.

    PubMed

    Del-Ben, C M; Ferreira, C A Q; Alves-Neto, W C; Graeff, F G

    2008-04-01

    Facial expressions of basic emotions have been widely used to investigate the neural substrates of emotion processing, but little is known about the exact meaning of subjective changes provoked by perceiving facial expressions. Our assumption was that fearful faces would be related to the processing of potential threats, whereas angry faces would be related to the processing of proximal threats. Experimental studies have suggested that serotonin modulates the brain processes underlying defensive responses to environmental threats, facilitating risk assessment behavior elicited by potential threats and inhibiting fight or flight responses to proximal threats. In order to test these predictions about the relationship between fearful and angry faces and defensive behaviors, we carried out a review of the literature about the effects of pharmacological probes that affect 5-HT-mediated neurotransmission on the perception of emotional faces. The hypothesis that angry faces would be processed as a proximal threat and that, as a consequence, their recognition would be impaired by an increase in 5-HT function was not supported by the results reviewed. In contrast, most of the studies that evaluated the behavioral effects of serotonin challenges showed that increased 5-HT neurotransmission facilitates the recognition of fearful faces, whereas its decrease impairs the same performance. These results agree with the hypothesis that fearful faces are processed as potential threats and that 5-HT enhances this brain processing.

  18. Face recognition using transform domain texture features

    NASA Astrophysics Data System (ADS)

    Rangaswamy, Y.; S K, Ramya; Raja, K. B.; K. R., Venugopal; Patnaik, L. M.

    2013-12-01

    The face recognition is an efficient biometric system to identify a person. In this paper, we propose Face Recognition using Transform Domain Texture Features (FRTDTF). The face images are preprocessed and two sets of texture features are extracted. In first feature set, the Discrete Wavelet Transform (DWT) is applied on face image and considered only high frequency sub band coefficients to extract edge information efficiently. The Dual Tree Complex Wavelet Transform (DTCWT) is applied on high frequency sub bands of DWT to derive Low and High frequency DTCWT coefficients. The texture features of DTCWT coefficients are computed using Overlapping Local Binary Pattern (OLBP) to generate feature set 1. In second feature set, the DTCWT is applied on preprocessed face image and considered all frequency sub bands coefficients to extract significant information and edge information of face image. The texture features of DTCWT matrix are computed using OLBP to generate feature set 2. The final feature set is the concatenation of feature set 1 and set 2. The Euclidian distance (ED) is used to compare test image features with features of face images in the database. It is observed that, the performance parameter values are better in the case of proposed algorithm compared to existing algorithms.

  19. Combined Dynamic Time Warping with Multiple Sensors for 3D Gesture Recognition.

    PubMed

    Choi, Hyo-Rim; Kim, TaeYong

    2017-08-17

    Cyber-physical systems, which closely integrate physical systems and humans, can be applied to a wider range of applications through user movement analysis. In three-dimensional (3D) gesture recognition, multiple sensors are required to recognize various natural gestures. Several studies have been undertaken in the field of gesture recognition; however, gesture recognition was conducted based on data captured from various independent sensors, which rendered the capture and combination of real-time data complicated. In this study, a 3D gesture recognition method using combined information obtained from multiple sensors is proposed. The proposed method can robustly perform gesture recognition regardless of a user's location and movement directions by providing viewpoint-weighted values and/or motion-weighted values. In the proposed method, the viewpoint-weighted dynamic time warping with multiple sensors has enhanced performance by preventing joint measurement errors and noise due to sensor measurement tolerance, which has resulted in the enhancement of recognition performance by comparing multiple joint sequences effectively.

  20. Good match exploration for infrared face recognition

    NASA Astrophysics Data System (ADS)

    Yang, Changcai; Zhou, Huabing; Sun, Sheng; Liu, Renfeng; Zhao, Ji; Ma, Jiayi

    2014-11-01

    Establishing good feature correspondence is a critical prerequisite and a challenging task for infrared (IR) face recognition. Recent studies revealed that the scale invariant feature transform (SIFT) descriptor outperforms other local descriptors for feature matching. However, it only uses local appearance information for matching, and hence inevitably leads to a number of false matches. To address this issue, this paper explores global structure information (GSI) among SIFT correspondences, and proposes a new method SIFT-GSI for good match exploration. This is achieved by fitting a smooth mapping function for the underlying correct matches, which involves softassign and deterministic annealing. Quantitative comparisons with state-of-the-art methods on a publicly available IR human face database demonstrate that SIFT-GSI significantly outperforms other methods for feature matching, and hence it is able to improve the reliability of IR face recognition systems.

  1. Face recognition with L1-norm subspaces

    NASA Astrophysics Data System (ADS)

    Maritato, Federica; Liu, Ying; Colonnese, Stefania; Pados, Dimitris A.

    2016-05-01

    We consider the problem of representing individual faces by maximum L1-norm projection subspaces calculated from available face-image ensembles. In contrast to conventional L2-norm subspaces, L1-norm subspaces are seen to offer significant robustness to image variations, disturbances, and rank selection. Face recognition becomes then the problem of associating a new unknown face image to the "closest," in some sense, L1 subspace in the database. In this work, we also introduce the concept of adaptively allocating the available number of principal components to different face image classes, subject to a given total number/budget of principal components. Experimental studies included in this paper illustrate and support the theoretical developments.

  2. 2D Feature Recognition And 3d Reconstruction In Solar Euv Images

    NASA Astrophysics Data System (ADS)

    Aschwanden, Markus J.

    2005-05-01

    EUV images show the solar corona in a typical temperature range of T >rsim 1 MK, which encompasses the most common coronal structures: loops, filaments, and other magnetic structures in active regions, the quiet Sun, and coronal holes. Quantitative analysis increasingly demands automated 2D feature recognition and 3D reconstruction, in order to localize, track, and monitor the evolution of such coronal structures. We discuss numerical tools that “fingerprint” curvi-linear 1D features (e.g., loops and filaments). We discuss existing finger-printing algorithms, such as the brightness-gradient method, the oriented-connectivity method, stereoscopic methods, time-differencing, and space time feature recognition. We discuss improved 2D feature recognition and 3D reconstruction techniques that make use of additional a priori constraints, using guidance from magnetic field extrapolations, curvature radii constraints, and acceleration and velocity constraints in time-dependent image sequences. Applications of these algorithms aid the analysis of SOHO/EIT, TRACE, and STEREO/SECCHI data, such as disentangling, 3D reconstruction, and hydrodynamic modeling of coronal loops, postflare loops, filaments, prominences, and 3D reconstruction of the coronal magnetic field in general.

  3. A new method of 3D scene recognition from still images

    NASA Astrophysics Data System (ADS)

    Zheng, Li-ming; Wang, Xing-song

    2014-04-01

    Most methods of monocular visual three dimensional (3D) scene recognition involve supervised machine learning. However, these methods often rely on prior knowledge. Specifically, they learn the image scene as part of a training dataset. For this reason, when the sampling equipment or scene is changed, monocular visual 3D scene recognition may fail. To cope with this problem, a new method of unsupervised learning for monocular visual 3D scene recognition is here proposed. First, the image is made using superpixel segmentation based on the CIELAB color space values L, a, and b and on the coordinate values x and y of pixels, forming a superpixel image with a specific density. Second, a spectral clustering algorithm based on the superpixels' color characteristics and neighboring relationships was used to reduce the dimensions of the superpixel image. Third, the fuzzy distribution density functions representing sky, ground, and façade are multiplied with the segment pixels, where the expectations of these segments are obtained. A preliminary classification of sky, ground, and façade is generated in this way. Fourth, the most accurate classification images of sky, ground, and façade were extracted through the tier-1 wavelet sampling and Manhattan direction feature. Finally, a depth perception map is generated based on the pinhole imaging model and the linear perspective information of ground surface. Here, 400 images of Make3D Image data from the Cornell University website were used to test the algorithm. The experimental results showed that this unsupervised learning method provides a more effective monocular visual 3D scene recognition model than other methods.

  4. Uniformly spaced 3D modeling of human face from two images using parallel particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Chang, Yau-Zen; Hou, Jung-Fu; Tsao, Yi Hsiang; Lee, Shih-Tseng

    2011-09-01

    This paper proposes a scheme for finding the correspondence between uniformly spaced locations on the images of human face captured from different viewpoints at the same instant. The correspondence is dedicated for 3D reconstruction to be used in the registration procedure for neurosurgery where the exposure to projectors must be seriously restricted. The approach utilizes structured light to enhance patterns on the images and is initialized with the scale-invariant feature transform (SIFT). Successive locations are found according to spatial order using a parallel version of the particle swarm optimization algorithm. Furthermore, false locations are singled out for correction by searching for outliers from fitted curves. Case studies show that the scheme is able to correctly generate 456 evenly spaced 3D coordinate points in 23 seconds from a single shot of projected human face using a PC with 2.66 GHz Intel Q9400 CPU and 4GB RAM.

  5. Face recognition fusing global and local features

    NASA Astrophysics Data System (ADS)

    Yu, Wei-Wei; Teng, Xiao-Long; Liu, Chong-Qing

    2006-01-01

    One of the main issues of face recognition is to extract features from face images, which include both local and global features. We present a novel method to perform feature fusion at the feature level. First, global features are extracted by principal component analysis (PCA), while local features are obtained by active appearance model (AAM) and Gabor wavelet transform (GWT). Second, two types of features are fused by weighted concatenation. Finally, Euclidean and feature distances of fused features are applied to carry out a nearest neighbor classifier. The method is evaluated by the recognition rates and computation cost over two face image databases [AR (created by A. Martinez and R. Benavente) and SJTU-IPPR (Shanghai JiaoTong University-Institute of Image Processing and Pattern Recognition)]. Compared with PCA and elastic bunch graph matching (EBGM), the presented method is more effective. Though the recognition rate of the presented method is not as good as nonlinear feature combination (NFC), low computation cost is its superiority. In addition, experimental results show that the novel method is robust to variations over time, expression, illumination, and pose to a certain extent.

  6. FaceWarehouse: a 3D facial expression database for visual computing.

    PubMed

    Cao, Chen; Weng, Yanlin; Zhou, Shun; Tong, Yiying; Zhou, Kun

    2014-03-01

    We present FaceWarehouse, a database of 3D facial expressions for visual computing applications. We use Kinect, an off-the-shelf RGBD camera, to capture 150 individuals aged 7-80 from various ethnic backgrounds. For each person, we captured the RGBD data of her different expressions, including the neutral expression and 19 other expressions such as mouth-opening, smile, kiss, etc. For every RGBD raw data record, a set of facial feature points on the color image such as eye corners, mouth contour, and the nose tip are automatically localized, and manually adjusted if better accuracy is required. We then deform a template facial mesh to fit the depth data as closely as possible while matching the feature points on the color image to their corresponding points on the mesh. Starting from these fitted face meshes, we construct a set of individual-specific expression blendshapes for each person. These meshes with consistent topology are assembled as a rank-3 tensor to build a bilinear face model with two attributes: identity and expression. Compared with previous 3D facial databases, for every person in our database, there is a much richer matching collection of expressions, enabling depiction of most human facial actions. We demonstrate the potential of FaceWarehouse for visual computing with four applications: facial image manipulation, face component transfer, real-time performance-based facial image animation, and facial animation retargeting from video to image.

  7. 3D Imaging for hand gesture recognition: Exploring the software-hardware interaction of current technologies

    NASA Astrophysics Data System (ADS)

    Periverzov, Frol; Ilieş, Horea T.

    2012-09-01

    Interaction with 3D information is one of the fundamental and most familiar tasks in virtually all areas of engineering and science. Several recent technological advances pave the way for developing hand gesture recognition capabilities available to all, which will lead to more intuitive and efficient 3D user interfaces (3DUI). These developments can unlock new levels of expression and productivity in all activities concerned with the creation and manipulation of virtual 3D shapes and, specifically, in engineering design. Building fully automated systems for tracking and interpreting hand gestures requires robust and efficient 3D imaging techniques as well as potent shape classifiers. We survey and explore current and emerging 3D imaging technologies, and focus, in particular, on those that can be used to build interfaces between the users' hands and the machine. The purpose of this paper is to categorize and highlight the relevant differences between these existing 3D imaging approaches in terms of the nature of the information provided, output data format, as well as the specific conditions under which these approaches yield reliable data. Furthermore we explore the impact of each of these approaches on the computational cost and reliability of the required image processing algorithms. Finally we highlight the main challenges and opportunities in developing natural user interfaces based on hand gestures, and conclude with some promising directions for future research. [Figure not available: see fulltext.

  8. 3D video analysis of the novel object recognition test in rats.

    PubMed

    Matsumoto, Jumpei; Uehara, Takashi; Urakawa, Susumu; Takamura, Yusaku; Sumiyoshi, Tomiki; Suzuki, Michio; Ono, Taketoshi; Nishijo, Hisao

    2014-10-01

    The novel object recognition (NOR) test has been widely used to test memory function. We developed a 3D computerized video analysis system that estimates nose contact with an object in Long Evans rats to analyze object exploration during NOR tests. The results indicate that the 3D system reproducibly and accurately scores the NOR test. Furthermore, the 3D system captures a 3D trajectory of the nose during object exploration, enabling detailed analyses of spatiotemporal patterns of object exploration. The 3D trajectory analysis revealed a specific pattern of object exploration in the sample phase of the NOR test: normal rats first explored the lower parts of objects and then gradually explored the upper parts. A systematic injection of MK-801 suppressed changes in these exploration patterns. The results, along with those of previous studies, suggest that the changes in the exploration patterns reflect neophobia to a novel object and/or changes from spatial learning to object learning. These results demonstrate that the 3D tracking system is useful not only for detailed scoring of animal behaviors but also for investigation of characteristic spatiotemporal patterns of object exploration. The system has the potential to facilitate future investigation of neural mechanisms underlying object exploration that result from dynamic and complex brain activity.

  9. A multibiometric face recognition fusion framework with template protection

    NASA Astrophysics Data System (ADS)

    Chindaro, S.; Deravi, F.; Zhou, Z.; Ng, M. W. R.; Castro Neves, M.; Zhou, X.; Kelkboom, E.

    2010-04-01

    In this work we present a multibiometric face recognition framework based on combining information from 2D with 3D facial features. The 3D biometrics channel is protected by a privacy enhancing technology, which uses error correcting codes and cryptographic primitives to safeguard the privacy of the users of the biometric system at the same time enabling accurate matching through fusion with 2D. Experiments are conducted to compare the matching performance of such multibiometric systems with the individual biometric channels working alone and with unprotected multibiometric systems. The results show that the proposed hybrid system incorporating template protection, match and in some cases exceed the performance of corresponding unprotected equivalents, in addition to offering the additional privacy protection.

  10. An Effective 3D Shape Descriptor for Object Recognition with RGB-D Sensors

    PubMed Central

    Liu, Zhong; Zhao, Changchen; Wu, Xingming; Chen, Weihai

    2017-01-01

    RGB-D sensors have been widely used in various areas of computer vision and graphics. A good descriptor will effectively improve the performance of operation. This article further analyzes the recognition performance of shape features extracted from multi-modality source data using RGB-D sensors. A hybrid shape descriptor is proposed as a representation of objects for recognition. We first extracted five 2D shape features from contour-based images and five 3D shape features over point cloud data to capture the global and local shape characteristics of an object. The recognition performance was tested for category recognition and instance recognition. Experimental results show that the proposed shape descriptor outperforms several common global-to-global shape descriptors and is comparable to some partial-to-global shape descriptors that achieved the best accuracies in category and instance recognition. Contribution of partial features and computational complexity were also analyzed. The results indicate that the proposed shape features are strong cues for object recognition and can be combined with other features to boost accuracy. PMID:28245553

  11. Double linear regression classification for face recognition

    NASA Astrophysics Data System (ADS)

    Feng, Qingxiang; Zhu, Qi; Tang, Lin-Lin; Pan, Jeng-Shyang

    2015-02-01

    A new classifier designed based on linear regression classification (LRC) classifier and simple-fast representation-based classifier (SFR), named double linear regression classification (DLRC) classifier, is proposed for image recognition in this paper. As we all know, the traditional LRC classifier only uses the distance between test image vectors and predicted image vectors of the class subspace for classification. And the SFR classifier uses the test image vectors and the nearest image vectors of the class subspace to classify the test sample. However, the DLRC classifier computes out the predicted image vectors of each class subspace and uses all the predicted vectors to construct a novel robust global space. Then, the DLRC utilizes the novel global space to get the novel predicted vectors of each class for classification. A mass number of experiments on AR face database, JAFFE face database, Yale face database, Extended YaleB face database, and PIE face database are used to evaluate the performance of the proposed classifier. The experimental results show that the proposed classifier achieves better recognition rate than the LRC classifier, SFR classifier, and several other classifiers.

  12. Three-dimensional face recognition in the presence of facial expressions: an annotated deformable model approach.

    PubMed

    Kakadiaris, Ioannis A; Passalis, Georgios; Toderici, George; Murtuza, Mohammed N; Lu, Yunliang; Karampatziakis, Nikos; Theoharis, Theoharis

    2007-04-01

    In this paper, we present the computational tools and a hardware prototype for 3D face recognition. Full automation is provided through the use of advanced multistage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact metadata. We present our results on the largest known, and now publicly available, Face Recognition Grand Challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, this is the highest performance reported on the FRGC v2 database for the 3D modality.

  13. Real-time 3D human pose recognition from reconstructed volume via voxel classifiers

    NASA Astrophysics Data System (ADS)

    Yoo, ByungIn; Choi, Changkyu; Han, Jae-Joon; Lee, Changkyo; Kim, Wonjun; Suh, Sungjoo; Park, Dusik; Kim, Junmo

    2014-03-01

    This paper presents a human pose recognition method which simultaneously reconstructs a human volume based on ensemble of voxel classifiers from a single depth image in real-time. The human pose recognition is a difficult task since a single depth camera can capture only visible surfaces of a human body. In order to recognize invisible (self-occluded) surfaces of a human body, the proposed algorithm employs voxel classifiers trained with multi-layered synthetic voxels. Specifically, ray-casting onto a volumetric human model generates a synthetic voxel, where voxel consists of a 3D position and ID corresponding to the body part. The synthesized volumetric data which contain both visible and invisible body voxels are utilized to train the voxel classifiers. As a result, the voxel classifiers not only identify the visible voxels but also reconstruct the 3D positions and the IDs of the invisible voxels. The experimental results show improved performance on estimating the human poses due to the capability of inferring the invisible human body voxels. It is expected that the proposed algorithm can be applied to many fields such as telepresence, gaming, virtual fitting, wellness business, and real 3D contents control on real 3D displays.

  14. Realistic texture extraction for 3D face models robust to self-occlusion

    NASA Astrophysics Data System (ADS)

    Qu, Chengchao; Monari, Eduardo; Schuchert, Tobias; Beyerer, Jürgen

    2015-02-01

    In the context of face modeling, probably the most well-known approach to represent 3D faces is the 3D Morphable Model (3DMM). When 3DMM is fitted to a 2D image, the shape as well as the texture and illumination parameters are simultaneously estimated. However, if real facial texture is needed, texture extraction from the 2D image is necessary. This paper addresses the possible problems in texture extraction of a single image caused by self-occlusion. Unlike common approaches that leverage the symmetric property of the face by mirroring the visible facial part, which is sensitive to inhomogeneous illumination, this work first generates a virtual texture map for the skin area iteratively by averaging the color of neighbored vertices. Although this step creates unrealistic, overly smoothed texture, illumination stays constant between the real and virtual texture. In the second pass, the mirrored texture is gradually blended with the real or generated texture according to the visibility. This scheme ensures a gentle handling of illumination and yet yields realistic texture. Because the blending area only relates to non-informative area, main facial features still have unique appearance in different face halves. Evaluation results reveal realistic rendering in novel poses robust to challenging illumination conditions and small registration errors.

  15. Learning invariant face recognition from examples.

    PubMed

    Müller, Marco K; Tremer, Michael; Bodenstein, Christian; Würtz, Rolf P

    2013-05-01

    Autonomous learning is demonstrated by living beings that learn visual invariances during their visual experience. Standard neural network models do not show this sort of learning. On the example of face recognition in different situations we propose a learning process that separates learning of the invariance proper from learning new instances of individuals. The invariance is learned by a set of examples called model, which contains instances of all situations. New instances are compared with these on the basis of rank lists, which allow generalization across situations. The result is also implemented as a spike-time-based neural network, which is shown to be robust against disturbances. The learning capability is demonstrated by recognition experiments on a set of standard face databases.

  16. [Photographic face recognition of cooperators vs. defectors].

    PubMed

    Tanida, Shigehito; Shimoma, Eri; Mashima, Rie; Ma, Lili; Yamagishi, Toshio

    2003-06-01

    Results of three experiments, in which a total of 167 students participated, consistently indicated that participants performed recognition tasks better for face photographs of defectors than those of cooperators. The face photographs used in the experiments were those of participants taken during prisoner's dilemma (PD) experiments a few years prior to the present study. In Experiments 1 and 2, photographs of cooperators and defectors in a one-shot PD experiment, taken after they had filled out a lengthy post-experimental questionnaire, were used. In Experiment 3, the photographs were those of high and low cooperators, taken at the moment of a cooperation or defection choice, respectively. Recognition was better for photographs that were judged unattractive than attractive. At the same time, it was better for photographs of less cooperative participants in the PD studies than those of more cooperative participants. Implications of the findings for Cosmides & Tooby's (1992) 'cheater-detection' module for social exchange domain were discussed.

  17. Infrared face recognition using linear subspace analysis

    NASA Astrophysics Data System (ADS)

    Ge, Wei; Wang, Dawei; Cheng, Yuqi; Zhu, Ming

    2009-10-01

    Infrared image offers the main advantage over visible image of being invariant to illumination changes for face recognition. In this paper, based on the introduction of main methods of linear subspace analysis, such as Principal Component Analysis (PCA) , Linear Discriminant Analysis(LDA) and Fast Independent Component Analysis (FastICA),the application of these methods to the recognition of infrared face images offered by OTCBVS workshop are investigated, and the advantages and disadvantages are compared. Experimental results show that the combination approach of PCA and LDA leads to better classification performance than single PCA approach or LDA approach, while the FastICA approach leads to the best classification performance with the improvement of nearly 5% compared with the combination approach.

  18. Generalized Hough transform based time invariant action recognition with 3D pose information

    NASA Astrophysics Data System (ADS)

    Muench, David; Huebner, Wolfgang; Arens, Michael

    2014-10-01

    Human action recognition has emerged as an important field in the computer vision community due to its large number of applications such as automatic video surveillance, content based video-search and human robot interaction. In order to cope with the challenges that this large variety of applications present, recent research has focused more on developing classifiers able to detect several actions in more natural and unconstrained video sequences. The invariance discrimination tradeoff in action recognition has been addressed by utilizing a Generalized Hough Transform. As a basis for action representation we transform 3D poses into a robust feature space, referred to as pose descriptors. For each action class a one-dimensional temporal voting space is constructed. Votes are generated from associating pose descriptors with their position in time relative to the end of an action sequence. Training data consists of manually segmented action sequences. In the detection phase valid human 3D poses are assumed as input, e.g. originating from 3D sensors or monocular pose reconstruction methods. The human 3D poses are normalized to gain view-independence and transformed into (i) relative limb-angle space to ensure independence of non-adjacent joints or (ii) geometric features. In (i) an action descriptor consists of the relative angles between limbs and their temporal derivatives. In (ii) the action descriptor consists of different geometric features. In order to circumvent the problem of time-warping we propose to use a codebook of prototypical 3D poses which is generated from sample sequences of 3D motion capture data. This idea is in accordance with the concept of equivalence classes in action space. Results of the codebook method are presented using the Kinect sensor and the CMU Motion Capture Database.

  19. Gender-Based Prototype Formation in Face Recognition

    ERIC Educational Resources Information Center

    Baudouin, Jean-Yves; Brochard, Renaud

    2011-01-01

    The role of gender categories in prototype formation during face recognition was investigated in 2 experiments. The participants were asked to learn individual faces and then to recognize them. During recognition, individual faces were mixed with faces, which were blended faces of same or different genders. The results of the 2 experiments showed…

  20. Gender-Based Prototype Formation in Face Recognition

    ERIC Educational Resources Information Center

    Baudouin, Jean-Yves; Brochard, Renaud

    2011-01-01

    The role of gender categories in prototype formation during face recognition was investigated in 2 experiments. The participants were asked to learn individual faces and then to recognize them. During recognition, individual faces were mixed with faces, which were blended faces of same or different genders. The results of the 2 experiments showed…

  1. Complex cell prototype representation for face recognition.

    PubMed

    Prssoa, L; Leitao, A P

    1999-01-01

    In this paper we propose a new face recognition system based on a biologically inspired filtering method. Our work differs from previous proposals in: 1) the multistage filtering method employed; 2) the pyramid structure used, and most importantly; 3) the prototype construction scheme to determine the models stored in memory. The method is much simpler than previous proposals and relatively inexpensive computationally, while attaining error rates as low as 5%, very close to the best reported results.

  2. Super-resolution benefit for face recognition

    NASA Astrophysics Data System (ADS)

    Hu, Shuowen; Maschal, Robert; Young, S. Susan; Hong, Tsai Hong; Phillips, Jonathon P.

    2011-06-01

    Vast amounts of video footage are being continuously acquired by surveillance systems on private premises, commercial properties, government compounds, and military installations. Facial recognition systems have the potential to identify suspicious individuals on law enforcement watchlists, but accuracy is severely hampered by the low resolution of typical surveillance footage and the far distance of suspects from the cameras. To improve accuracy, super-resolution can enhance suspect details by utilizing a sequence of low resolution frames from the surveillance footage to reconstruct a higher resolution image for input into the facial recognition system. This work measures the improvement of face recognition with super-resolution in a realistic surveillance scenario. Low resolution and super-resolved query sets are generated using a video database at different eye-to-eye distances corresponding to different distances of subjects from the camera. Performance of a face recognition algorithm using the super-resolved and baseline query sets was calculated by matching against galleries consisting of frontal mug shots. The results show that super-resolution improves performance significantly at the examined mid and close ranges.

  3. Face recognition: a model specific ability.

    PubMed

    Wilmer, Jeremy B; Germine, Laura T; Nakayama, Ken

    2014-01-01

    In our everyday lives, we view it as a matter of course that different people are good at different things. It can be surprising, in this context, to learn that most of what is known about cognitive ability variation across individuals concerns the broadest of all cognitive abilities; an ability referred to as general intelligence, general mental ability, or just g. In contrast, our knowledge of specific abilities, those that correlate little with g, is severely constrained. Here, we draw upon our experience investigating an exceptionally specific ability, face recognition, to make the case that many specific abilities could easily have been missed. In making this case, we derive key insights from earlier false starts in the measurement of face recognition's variation across individuals, and we highlight the convergence of factors that enabled the recent discovery that this variation is specific. We propose that the case of face recognition ability illustrates a set of tools and perspectives that could accelerate fruitful work on specific cognitive abilities. By revealing relatively independent dimensions of human ability, such work would enhance our capacity to understand the uniqueness of individual minds.

  4. Comparing human and automatic face recognition performance.

    PubMed

    Adler, Andy; Schuckers, Michael E

    2007-10-01

    Face recognition technologies have seen dramatic improvements in performance over the past decade, and such systems are now widely used for security and commercial applications. Since recognizing faces is a task that humans are understood to be very good at, it is common to want to compare automatic face recognition (AFR) and human face recognition (HFR) in terms of biometric performance. This paper addresses this question by: 1) conducting verification tests on volunteers (HFR) and commercial AFR systems and 2) developing statistical methods to support comparison of the performance of different biometric systems. HFR was tested by presenting face-image pairs and asking subjects to classify them on a scale of "Same," "Probably Same," "Not sure," "Probably Different," and "Different"; the same image pairs were presented to AFR systems, and the biometric match score was measured. To evaluate these results, two new statistical evaluation techniques are developed. The first is a new way to normalize match-score distributions, where a normalized match score t is calculated as a function of the angle from a representation of [false match rate, false nonmatch rate] values in polar coordinates from some center. Using this normalization, we develop a second methodology to calculate an average detection error tradeoff (DET) curve and show that this method is equivalent to direct averaging of DET data along each angle from the center. This procedure is then applied to compare the performance of the best AFR algorithms available to us in the years 1999, 2001, 2003, 2005, and 2006, in comparison to human scores. Results show that algorithms have dramatically improved in performance over that time. In comparison to the performance of the best AFR system of 2006, 29.2% of human subjects performed better, while 37.5% performed worse.

  5. Block error correction codes for face recognition

    NASA Astrophysics Data System (ADS)

    Hussein, Wafaa R.; Sellahewa, Harin; Jassim, Sabah A.

    2011-06-01

    Face recognition is one of the most desirable biometric-based authentication schemes to control access to sensitive information/locations and as a proof of identity to claim entitlement to services. The aim of this paper is to develop block-based mechanisms, to reduce recognition errors that result from varying illumination conditions with emphasis on using error correction codes. We investigate the modelling of error patterns in different parts/blocks of face images as a result of differences in illumination conditions, and we use appropriate error correction codes to deal with the corresponding distortion. We test the performance of our proposed schemes using the Extended Yale-B Face Database, which consists of face images belonging to 5 illumination subsets depending on the direction of light source from the camera. In our experiments each image is divided into three horizontal regions as follows: region1, three rows above the eyebrows, eyebrows and eyes; region2, nose region and region3, mouth and chin region. By estimating statistical parameters for errors in each region we select suitable BCH error correction codes that yield improved recognition accuracy for that particular region in comparison to applying error correction codes to the entire image. Discrete Wavelet Transform (DWT) to a depth of 3 is used for face feature extraction, followed by global/local binarization of coefficients in each subbands. We shall demonstrate that the use of BCH improves separation of the distribution of Hamming distances of client-client samples from the distribution of Hamming distances of imposter-client samples.

  6. 3D imaging by serial block face scanning electron microscopy for materials science using ultramicrotomy.

    PubMed

    Hashimoto, Teruo; Thompson, George E; Zhou, Xiaorong; Withers, Philip J

    2016-04-01

    Mechanical serial block face scanning electron microscopy (SBFSEM) has emerged as a means of obtaining three dimensional (3D) electron images over volumes much larger than possible by focused ion beam (FIB) serial sectioning and at higher spatial resolution than achievable with conventional X-ray computed tomography (CT). Such high resolution 3D electron images can be employed for precisely determining the shape, volume fraction, distribution and connectivity of important microstructural features. While soft (fixed or frozen) biological samples are particularly well suited for nanoscale sectioning using an ultramicrotome, the technique can also produce excellent 3D images at electron microscope resolution in a time and resource-efficient manner for engineering materials. Currently, a lack of appreciation of the capabilities of ultramicrotomy and the operational challenges associated with minimising artefacts for different materials is limiting its wider application to engineering materials. Consequently, this paper outlines the current state of the art for SBFSEM examining in detail how damage is introduced during slicing and highlighting strategies for minimising such damage. A particular focus of the study is the acquisition of 3D images for a variety of metallic and coated systems. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  7. Face recognition using 4-PSK joint transform correlation

    NASA Astrophysics Data System (ADS)

    Moniruzzaman, Md.; Alam, Mohammad S.

    2016-04-01

    This paper presents an efficient phase-encoded and 4-phase shift keying (PSK)-based fringe-adjusted joint transform correlation (FJTC) technique for face recognition applications. The proposed technique uses phase encoding and a 4- channel phase shifting method on the reference image which can be pre-calculated without affecting the system processing speed. The 4-channel PSK step eliminates the unwanted zero-order term, autocorrelation among multiple similar input scene objects while yield enhanced cross-correlation output. For each channel, discrete wavelet decomposition preprocessing has been used to accommodate the impact of various 3D facial expressions, effects of noise, and illumination variations. The performance of the proposed technique has been tested using various image datasets such as Yale, and extended Yale B under different environments such as illumination variation and 3D changes in facial expressions. The test results show that the proposed technique yields significantly better performance when compared to existing JTC-based face recognition techniques.

  8. Semantic information can facilitate covert face recognition in congenital prosopagnosia.

    PubMed

    Rivolta, Davide; Schmalzl, Laura; Coltheart, Max; Palermo, Romina

    2010-11-01

    People with congenital prosopagnosia have never developed the ability to accurately recognize faces. This single case investigation systematically investigates covert and overt face recognition in "C.," a 69 year-old woman with congenital prosopagnosia. Specifically, we: (a) describe the first assessment of covert face recognition in congenital prosopagnosia using multiple tasks; (b) show that semantic information can contribute to covert recognition; and (c) provide a theoretical explanation for the mechanisms underlying covert face recognition.

  9. Varying face occlusion detection and iterative recovery for face recognition

    NASA Astrophysics Data System (ADS)

    Wang, Meng; Hu, Zhengping; Sun, Zhe; Zhao, Shuhuan; Sun, Mei

    2017-05-01

    In most sparse representation methods for face recognition (FR), occlusion problems were usually solved via removing the occlusion part of both query samples and training samples to perform the recognition process. This practice ignores the global feature of facial image and may lead to unsatisfactory results due to the limitation of local features. Considering the aforementioned drawback, we propose a method called varying occlusion detection and iterative recovery for FR. The main contributions of our method are as follows: (1) to detect an accurate occlusion area of facial images, an image processing and intersection-based clustering combination method is used for occlusion FR; (2) according to an accurate occlusion map, the new integrated facial images are recovered iteratively and put into a recognition process; and (3) the effectiveness on recognition accuracy of our method is verified by comparing it with three typical occlusion map detection methods. Experiments show that the proposed method has a highly accurate detection and recovery performance and that it outperforms several similar state-of-the-art methods against partial contiguous occlusion.

  10. Three-dimensional object recognition using gradient descent and the universal 3-D array grammar

    NASA Astrophysics Data System (ADS)

    Baird, Leemon C., III; Wang, Patrick S. P.

    1992-02-01

    A new algorithm is presented for applying Marill's minimum standard deviation of angles (MSDA) principle for interpreting line drawings without models. Even though no explicit models or additional heuristics are included, the algorithm tends to reach the same 3-D interpretations of 2-D line drawings that humans do. Marill's original algorithm repeatedly generated a set of interpretations and chose the one with the lowest standard deviation of angles (SDA). The algorithm presented here explicitly calculates the partial derivatives of SDA with respect to all adjustable parameters, and follows this gradient to minimize SDA. For a picture with lines meeting at m points forming n angles, the gradient descent algorithm requires O(n) time to adjust all the points, while the original algorithm required O(mn) time to do so. For the pictures described by Marill, this gradient descent algorithm running on a Macintosh II was found to be one to two orders of magnitude faster than the original algorithm running on a Symbolics, while still giving comparable results. Once the 3-D interpretation of the line drawing has been found, the 3-D object can be reduced to a description string using the Universal 3-D Array Grammar. This is a general grammar which allows any connected object represented as a 3-D array of pixels to be reduced to a description string. The algorithm based on this grammar is well suited to parallel computation, and could run efficiently on parallel hardware. This paper describes both the MSDA gradient descent algorithm and the Universal 3-D Array Grammar algorithm. Together, they transform a 2-D line drawing represented as a list of line segments into a string describing the 3-D object pictured. The strings could then be used for object recognition, learning, or storage for later manipulation.

  11. Staining and embedding of human chromosomes for 3-d serial block-face scanning electron microscopy.

    PubMed

    Yusuf, Mohammed; Chen, Bo; Hashimoto, Teruo; Estandarte, Ana Katrina; Thompson, George; Robinson, Ian

    2014-12-01

    The high-order structure of human chromosomes is an important biological question that is still under investigation. Studies have been done on imaging human mitotic chromosomes using mostly 2-D microscopy methods. To image micron-sized human chromosomes in 3-D, we developed a procedure for preparing samples for serial block-face scanning electron microscopy (SBFSEM). Polyamine chromosomes are first separated using a simple filtration method and then stained with heavy metal. We show that the DNA-specific platinum blue provides higher contrast than osmium tetroxide. A two-step procedure for embedding chromosomes in resin is then used to concentrate the chromosome samples. After stacking the SBFSEM images, a familiar X-shaped chromosome was observed in 3-D.

  12. Robust and Blind 3D Mesh Watermarking in Spatial Domain Based on Faces Categorization and Sorting

    NASA Astrophysics Data System (ADS)

    Molaei, Amir Masoud; Ebrahimnezhad, Hossein; Sedaaghi, Mohammad Hossein

    2016-06-01

    In this paper, a 3D watermarking algorithm in spatial domain is presented with blind detection. In the proposed method, a negligible visual distortion is observed in host model. Initially, a preprocessing is applied on the 3D model to make it robust against geometric transformation attacks. Then, a number of triangle faces are determined as mark triangles using a novel systematic approach in which faces are categorized and sorted robustly. In order to enhance the capability of information retrieval by attacks, block watermarks are encoded using Reed-Solomon block error-correcting code before embedding into the mark triangles. Next, the encoded watermarks are embedded in spherical coordinates. The proposed method is robust against additive noise, mesh smoothing and quantization attacks. Also, it is stout next to geometric transformation, vertices and faces reordering attacks. Moreover, the proposed algorithm is designed so that it is robust against the cropping attack. Simulation results confirm that the watermarked models confront very low distortion if the control parameters are selected properly. Comparison with other methods demonstrates that the proposed method has good performance against the mesh smoothing attacks.

  13. The neural speed of familiar face recognition.

    PubMed

    Barragan-Jason, G; Cauchoix, M; Barbeau, E J

    2015-08-01

    Rapidly recognizing familiar people from their faces appears critical for social interactions (e.g., to differentiate friend from foe). However, the actual speed at which the human brain can distinguish familiar from unknown faces still remains debated. In particular, it is not clear whether familiarity can be extracted from rapid face individualization or if it requires additional time consuming processing. We recorded scalp EEG activity in 28 subjects performing a go/no-go, famous/non-famous, unrepeated, face recognition task. Speed constraints were used to encourage subjects to use the earliest familiarity information available. Event related potential (ERP) analyses show that both the N170 and the N250 components were modulated by familiarity. The N170 modulation was related to behaviour: subjects presenting the strongest N170 modulation were also faster but less accurate than those who only showed weak N170 modulation. A complementary Multi-Variate Pattern Analysis (MVPA) confirmed ERP results and provided some more insights into the dynamics of face recognition as the N170 differential effect appeared to be related to a first transitory phase (transitory bump of decoding power) starting at around 140 ms, which returned to baseline afterwards. This bump of activity was henceforth followed by an increase of decoding power starting around 200 ms after stimulus onset. Overall, our results suggest that rather than a simple single-process, familiarity for faces may rely on a cascade of neural processes, including a coarse and fast stage starting at 140 ms and a more refined but slower stage occurring after 200 ms.

  14. Three-dimensional recording of the human face with a 3D laser scanner.

    PubMed

    Kovacs, L; Zimmermann, A; Brockmann, G; Gühring, M; Baurecht, H; Papadopulos, N A; Schwenzer-Zimmerer, K; Sader, R; Biemer, E; Zeilhofer, H F

    2006-01-01

    Three-dimensional recording of the surface of the human body or of certain anatomical areas has gained an ever increasing importance in recent years. When recording living surfaces, such as the human face, not only has a varying degree of surface complexity to be accounted for, but also a variety of other factors, such as motion artefacts. It is of importance to establish standards for the recording procedure, which will optimise results and allow for better comparison and validation. In the study presented here, the faces of five male test persons were scanned in different experimental settings using non-contact 3D digitisers, type Minolta Vivid 910). Among others, the influence of the number of scanners used, the angle of recording, the head position of the test person, the impact of the examiner and of examination time on accuracy and precision of the virtual face models generated from the scanner data with specialised software were investigated. Computed data derived from the virtual models were compared to corresponding reference measurements carried out manually between defined landmarks on the test persons' faces. We describe experimental conditions that were of benefit in optimising the quality of scanner recording and the reliability of three-dimensional surface imaging. However, almost 50% of distances between landmarks derived from the virtual models deviated more than 2mm from the reference of manual measurements on the volunteers' faces.

  15. Statistical and neural network classifiers in model-based 3-D object recognition

    NASA Astrophysics Data System (ADS)

    Newton, Scott C.; Nutter, Brian S.; Mitra, Sunanda

    1991-02-01

    For autonomous machines equipped with vision capabilities and in a controlled environment 3-D model-based object identification methodologies will in general solve rigid body recognition problems. In an uncontrolled environment however several factors pose difficulties for correct identification. We have addressed the problem of 3-D object recognition using a number of methods including neural network classifiers and a Bayesian-like classifier for matching image data with model projection-derived data [1 21. Neural network classifiers used began operation as simple feature vector classifiers. However unmodelled signal behavior was learned with additional samples yielding great improvement in classification rates. The model analysis drastically shortened training time of both classification systems. In an environment where signal behavior is not accurately modelled two separate forms of learning give the systems the ability to update estimates of this behavior. Required of course are sufficient samples to learn this new information. Given sufficient information and a well-controlled environment identification of 3-D objects from a limited number of classes is indeed possible. 1.

  16. Complex Wavelet Transform-Based Face Recognition

    NASA Astrophysics Data System (ADS)

    Eleyan, Alaa; Özkaramanli, Hüseyin; Demirel, Hasan

    2009-12-01

    Complex approximately analytic wavelets provide a local multiscale description of images with good directional selectivity and invariance to shifts and in-plane rotations. Similar to Gabor wavelets, they are insensitive to illumination variations and facial expression changes. The complex wavelet transform is, however, less redundant and computationally efficient. In this paper, we first construct complex approximately analytic wavelets in the single-tree context, which possess Gabor-like characteristics. We, then, investigate the recently developed dual-tree complex wavelet transform (DT-CWT) and the single-tree complex wavelet transform (ST-CWT) for the face recognition problem. Extensive experiments are carried out on standard databases. The resulting complex wavelet-based feature vectors are as discriminating as the Gabor wavelet-derived features and at the same time are of lower dimension when compared with that of Gabor wavelets. In all experiments, on two well-known databases, namely, FERET and ORL databases, complex wavelets equaled or surpassed the performance of Gabor wavelets in recognition rate when equal number of orientations and scales is used. These findings indicate that complex wavelets can provide a successful alternative to Gabor wavelets for face recognition.

  17. Face recognition: a model specific ability

    PubMed Central

    Wilmer, Jeremy B.; Germine, Laura T.; Nakayama, Ken

    2014-01-01

    In our everyday lives, we view it as a matter of course that different people are good at different things. It can be surprising, in this context, to learn that most of what is known about cognitive ability variation across individuals concerns the broadest of all cognitive abilities; an ability referred to as general intelligence, general mental ability, or just g. In contrast, our knowledge of specific abilities, those that correlate little with g, is severely constrained. Here, we draw upon our experience investigating an exceptionally specific ability, face recognition, to make the case that many specific abilities could easily have been missed. In making this case, we derive key insights from earlier false starts in the measurement of face recognition’s variation across individuals, and we highlight the convergence of factors that enabled the recent discovery that this variation is specific. We propose that the case of face recognition ability illustrates a set of tools and perspectives that could accelerate fruitful work on specific cognitive abilities. By revealing relatively independent dimensions of human ability, such work would enhance our capacity to understand the uniqueness of individual minds. PMID:25346673

  18. Improving automatic face recognition with user interaction.

    PubMed

    Arca, Stefano; Campadelli, Paola; Lanzarotti, Raffaella; Lipori, Giuseppe; Cervelli, Federico; Mattei, Aldo

    2012-05-01

    Face recognition systems aim to recognize the identity of a person depicted in a photograph by comparing it against a gallery of prerecorded images. Current systems perform quite well in controlled scenarios, but they allow for none or little interaction in case of mistakes due to the low quality of images or to algorithmic limitations. Following the needs and suggestions of investigators, we present a guided user interface that allows to adjust from a fully automatic to a fully assisted modality of execution, according to the difficulty of the task and to amount of available information (gender, age, etc.): the user can generally rely on automatic execution and intervene only on a limited number of examples when a failure is automatically detected or when the quality of intermediate results is deemed unsatisfactory. The interface runs on top of a preexistent automatic face recognition algorithm in such a way to guarantee full control over the execution flow and to exploit the peculiarities of the underlying image processing techniques. The viability of the proposed solution is tested on a classic face identification task run on a standard publicly available database (the XM2VTS), assessing the improvement to user interaction over the automatic system performance. © 2011 American Academy of Forensic Sciences.

  19. Prediction of 3D chip formation in the facing cutting with lathe machine using FEM

    NASA Astrophysics Data System (ADS)

    Prasetyo, Yudhi; Tauviqirrahman, Mohamad; Rusnaldy

    2016-04-01

    This paper presents the prediction of the chip formation at the machining process using a lathe machine in a more specific way focusing on facing cutting (face turning). The main purpose is to propose a new approach to predict the chip formation with the variation of the cutting directions i.e., the backward and forward direction. In addition, the interaction between stress analysis and chip formation on cutting process was also investigated. The simulations were conducted using three dimensional (3D) finite element method based on ABAQUS software with aluminum and high speed steel (HSS) as the workpiece and the tool materials, respectively. The simulation result showed that the chip resulted using a backward direction depicts a better formation than that using a conventional (forward) direction.

  20. a Review on State-Of Face Recognition Approaches

    NASA Astrophysics Data System (ADS)

    Mahmood, Zahid; Muhammad, Nazeer; Bibi, Nargis; Ali, Tauseef

    Automatic Face Recognition (FR) presents a challenging task in the field of pattern recognition and despite the huge research in the past several decades; it still remains an open research problem. This is primarily due to the variability in the facial images, such as non-uniform illuminations, low resolution, occlusion, and/or variation in poses. Due to its non-intrusive nature, the FR is an attractive biometric modality and has gained a lot of attention in the biometric research community. Driven by the enormous number of potential application domains, many algorithms have been proposed for the FR. This paper presents an overview of the state-of-the-art FR algorithms, focusing their performances on publicly available databases. We highlight the conditions of the image databases with regard to the recognition rate of each approach. This is useful as a quick research overview and for practitioners as well to choose an algorithm for their specified FR application. To provide a comprehensive survey, the paper divides the FR algorithms into three categories: (1) intensity-based, (2) video-based, and (3) 3D based FR algorithms. In each category, the most commonly used algorithms and their performance is reported on standard face databases and a brief critical discussion is carried out.

  1. Possible 3D out-of-plane target recognition with an optimized template function

    NASA Astrophysics Data System (ADS)

    Chen, Chulung; Fang, Jian-Shuen; Wu, Chih-Sung

    2001-09-01

    We present a possible way to detect 3D out-of-plane targets. Several Su-27 airplane images with different 3D rotational views were used to synthesize a template function, which successfully detects the target against the non-target such as the F16 airplane. A theoretical development for the purpose of pattern recognition is proposed. The system has the desirable property of sharp peaks with low sidelobes in the output correlation plane when multiple targets appear in the input. The test results show that the correlation peak is quite distinguishable at the location of the target and indicate the success of the technique. When combining the advantages of optics and electronics, the system is suitable for hybrid optical/electrical signal processing.

  2. Artificial neural networks and model-based recognition of 3-D objects from 2-D images

    NASA Astrophysics Data System (ADS)

    Chao, Chih-Ho; Dhawan, Atam P.

    1992-09-01

    A computer vision system is developed for 3-D object recognition using artificial neural networks and a knowledge-based top-down feedback analysis system. This computer vision system can adequately analyze an incomplete edge map provided by a low-level processor for 3-D representation and recognition using key features. The key features are selected using a priority assignment and then used in an artificial neural network for matching with model key features. The result of such matching is utilized in generating the model-driven top-down feedback analysis. From the incomplete edge map we try to pick a candidate pattern utilizing the key feature priority assignment. The highest priority is given for the most connected node and associated features. The features are space invariant structures and sets of orientation for edge primitives. These features are now mapped into real numbers. A Hopfield network is then applied with two levels of matching to reduce the search time. The first match is to choose the class of possible model, the second match is then to find the model closest to the data patterns. This model is then rotated in 3-D to find the best match with the incomplete edge patterns and to provide the additional features in 3-D. In the case of multiple objects, a dynamically interconnected search strategy is designed to recognize objects using one pattern at a time. This strategy is also useful in recognizing occluded objects. The experimental results presented show the capability and effectiveness of this system.

  3. 3D landmarking in multiexpression face analysis: a preliminary study on eyebrows and mouth.

    PubMed

    Vezzetti, Enrico; Marcolin, Federica

    2014-08-01

    The application of three-dimensional (3D) facial analysis and landmarking algorithms in the field of maxillofacial surgery and other medical applications, such as diagnosis of diseases by facial anomalies and dysmorphism, has gained a lot of attention. In a previous work, we used a geometric approach to automatically extract some 3D facial key points, called landmarks, working in the differential geometry domain, through the coefficients of fundamental forms, principal curvatures, mean and Gaussian curvatures, derivatives, shape and curvedness indexes, and tangent map. In this article we describe the extension of our previous landmarking algorithm, which is now able to extract eyebrows and mouth landmarks using both old and new meshes. The algorithm has been tested on our face database and on the public Bosphorus 3D database. We chose to work on the mouth and eyebrows as a separate study because of the role that these parts play in facial expressions. In fact, since the mouth is the part of the face that moves the most and affects mainly facial expressions, extracting mouth landmarks from various facial poses means that the newly developed algorithm is pose-independent. This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors http://www.springer.com/00266 .

  4. 3D-silicon- and recognition-based logic: enabling the road to HAL

    NASA Astrophysics Data System (ADS)

    Carson, John C.

    2000-11-01

    HAL, the disembodied robotic voice ever present in the Arthur C. Clark movie 2001, A Space Odyssey, is the archetype for artificial intelligence. What will it take to achieve HAL? Trillions of highly interconnected arithmetic units in very close proximity is mandatory. Moore's Law and 3D SILICON will get that part done. Linguistic articulation of experience and direction of action is the other missing piece. The human version's neural circuits are basically just multiply-and-add template matchers and yet verbalization of experience is the apparently automatic result. We call this recognition based logic and by embedding it in a properly interconnected processor network, the capabilities of HAL will be achieved.

  5. The Significance of Hair for Face Recognition

    PubMed Central

    Toseeb, Umar; Keeble, David R. T.; Bryant, Eleanor J.

    2012-01-01

    Hair is a feature of the head that frequently changes in different situations. For this reason much research in the area of face perception has employed stimuli without hair. To investigate the effect of the presence of hair we used faces with and without hair in a recognition task. Participants took part in trials in which the state of the hair either remained consistent (Same) or switched between learning and test (Switch). It was found that in the Same trials performance did not differ for stimuli presented with and without hair. This implies that there is sufficient information in the internal features of the face for optimal performance in this task. It was also found that performance in the Switch trials was substantially lower than in the Same trials. This drop in accuracy when the stimuli were switched suggests that faces are represented in a holistic manner and that manipulation of the hair causes disruption to this, with implications for the interpretation of some previous studies. PMID:22461902

  6. Face recognition from a moving platform via sparse representation

    NASA Astrophysics Data System (ADS)

    Hsu, Ming Kai; Hsu, Charles; Lee, Ting N.; Szu, Harold

    2012-06-01

    A video-based surveillance system for passengers includes face detection, face tracking and face recognition. In general, the final recognition result of the video-based surveillance system is usually determined by the cumulative recognition results. Under this strategy, the correctness of face tracking plays an important role for the system recognition rate. For face tracking, the challenges of face tracking on a moving platform are that the space and time information used for conventional face tracking algorithms may be lost. Consequently, conventional face tracking algorithms can barely handle the face tracking on a moving platform. In this paper, we have verified the state-of-the-art technologies for face detection, face tracking and face recognition on a moving platform. In the mean time, we also proposed a new strategy for face tracking on a moving platform or face tracking under very low frame rate. The steps of the new strategy for face detection are: (1) classification the detected faces over a certain period instead of every frame (2) Tracking of each passenger is equivalent to reconstruct the time order of certain period for each passenger. If the cumulative recognition results are the only part needed for the surveillance system, step 2 can be skipped. In addition, if the additional information from the passengers is required, such as path tracking, lip read, gesture recognition, etc, time order reconstruction in step 2 can offer the information required.

  7. Performance of a neural-network-based 3-D object recognition system

    NASA Astrophysics Data System (ADS)

    Rak, Steven J.; Kolodzy, Paul J.

    1991-08-01

    Object recognition in laser radar sensor imagery is a challenging application of neural networks. The task involves recognition of objects at a variety of distances and aspects with significant levels of sensor noise. These variables are related to sensor parameters such as sensor signal strength and angular resolution, as well as object range and viewing aspect. The effect of these parameters on a fixed recognition system based on log-polar mapped features and an unsupervised neural network classifier are investigated. This work is an attempt to quantify the design parameters of a laser radar measurement system with respect to classifying and/or identifying objects by the shape of their silhouettes. Experiments with vehicle silhouettes rotated through 90 deg-of-view angle from broadside to head-on ('out-of-plane' rotation) have been used to quantify the performance of a log-polar map/neural-network based 3-D object recognition system. These experiments investigated several key issues such as category stability, category memory compression, image fidelity, and viewing aspect. Initial results indicate a compression from 720 possible categories (8 vehicles X 90 out-of-plane rotations) to a classifier memory with approximately 30 stable recognition categories. These results parallel the human experience of studying an object from several viewing angles yet recognizing it through a wide range of viewing angles. Results are presented illustrating category formation for an eight vehicle dataset as a function of several sensor parameters. These include: (1) sensor noise, as a function of carrier-to-noise ratio; (2) pixels on the vehicle, related to angular resolution and target range; and (3) viewing aspect, as related to sensor-to-platform depression angle. This work contributes to the formation of a three- dimensional object recognition system.

  8. Neural systems for recognition of emotional prosody: a 3-D lesion study.

    PubMed

    Adolphs, Ralph; Damasio, Hanna; Tranel, Daniel

    2002-03-01

    Which brain regions are associated with recognition of emotional prosody? Are these distinct from those for recognition of facial expression? These issues were investigated by mapping the overlaps of co-registered lesions from 66 brain-damaged participants as a function of their performance in rating basic emotions. It was found that recognizing emotions from prosody draws on the right frontoparietal operculum, the bilateral frontal pole, and the left frontal operculum. Recognizing emotions from prosody and facial expressions draws on the right frontoparietal cortex, which may be important in reconstructing aspects of the emotion signaled by the stimulus. Furthermore, there were regions in the left and right temporal lobes that contributed disproportionately to recognition of emotion from faces or prosody, respectively.

  9. Towards Robust Face Recognition from Video

    SciTech Connect

    Price, JR

    2001-10-18

    A novel, template-based method for face recognition is presented. The goals of the proposed method are to integrate multiple observations for improved robustness and to provide auxiliary confidence data for subsequent use in an automated video surveillance system. The proposed framework consists of a parallel system of classifiers, referred to as observers, where each observer is trained on one face region. The observer outputs are combined to yield the final recognition result. Three of the four confounding factors--expression, illumination, and decoration--are specifically addressed in this paper. The extension of the proposed approach to address the fourth confounding factor--pose--is straightforward and well supported in previous work. A further contribution of the proposed approach is the computation of a revealing confidence measure. This confidence measure will aid the subsequent application of the proposed method to video surveillance scenarios. Results are reported for a database comprising 676 images of 160 subjects under a variety of challenging circumstances. These results indicate significant performance improvements over previous methods and demonstrate the usefulness of the confidence data.

  10. Graphical Representation for Heterogeneous Face Recognition.

    PubMed

    Peng, Chunlei; Gao, Xinbo; Wang, Nannan; Li, Jie

    2017-02-01

    Heterogeneous face recognition (HFR) refers to matching face images acquired from different sources (i.e., different sensors or different wavelengths) for identification. HFR plays an important role in both biometrics research and industry. In spite of promising progresses achieved in recent years, HFR is still a challenging problem due to the difficulty to represent two heterogeneous images in a homogeneous manner. Existing HFR methods either represent an image ignoring the spatial information, or rely on a transformation procedure which complicates the recognition task. Considering these problems, we propose a novel graphical representation based HFR method (G-HFR) in this paper. Markov networks are employed to represent heterogeneous image patches separately, which takes the spatial compatibility between neighboring image patches into consideration. A coupled representation similarity metric (CRSM) is designed to measure the similarity between obtained graphical representations. Extensive experiments conducted on multiple HFR scenarios (viewed sketch, forensic sketch, near infrared image, and thermal infrared image) show that the proposed method outperforms state-of-the-art methods.

  11. Impaired face recognition is associated with social inhibition

    PubMed Central

    Avery, Suzanne N; VanDerKlok, Ross M; Heckers, Stephan; Blackford, Jennifer U

    2016-01-01

    Face recognition is fundamental to successful social interaction. Individuals with deficits in face recognition are likely to have social functioning impairments that may lead to heightened risk for social anxiety. A critical component of social interaction is how quickly a face is learned during initial exposure to a new individual. Here, we used a novel Repeated Faces task to assess how quickly memory for faces is established. Face recognition was measured over multiple exposures in 52 young adults ranging from low to high in social inhibition, a core dimension of social anxiety. High social inhibition was associated with a smaller slope of change in recognition memory over repeated face exposure, indicating participants with higher social inhibition showed smaller improvements in recognition memory after seeing faces multiple times. We propose that impaired face learning is an important mechanism underlying social inhibition and may contribute to, or maintain, social anxiety. PMID:26776300

  12. Impaired face recognition is associated with social inhibition.

    PubMed

    Avery, Suzanne N; VanDerKlok, Ross M; Heckers, Stephan; Blackford, Jennifer U

    2016-02-28

    Face recognition is fundamental to successful social interaction. Individuals with deficits in face recognition are likely to have social functioning impairments that may lead to heightened risk for social anxiety. A critical component of social interaction is how quickly a face is learned during initial exposure to a new individual. Here, we used a novel Repeated Faces task to assess how quickly memory for faces is established. Face recognition was measured over multiple exposures in 52 young adults ranging from low to high in social inhibition, a core dimension of social anxiety. High social inhibition was associated with a smaller slope of change in recognition memory over repeated face exposure, indicating participants with higher social inhibition showed smaller improvements in recognition memory after seeing faces multiple times. We propose that impaired face learning is an important mechanism underlying social inhibition and may contribute to, or maintain, social anxiety. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  13. 3D CARS image reconstruction and pattern recognition on SHG images

    NASA Astrophysics Data System (ADS)

    Medyukhina, Anna; Vogler, Nadine; Latka, Ines; Dietzek, Benjamin; Cicchi, Riccardo; Pavone, Francesco S.; Popp, Jürgen

    2012-06-01

    Nonlinear optical imaging techniques based e.g. on coherent anti-Stokes Raman scattering (CARS) or second-harmonic generation (SHG) show great potential for in-vivo investigations of tissue. While the microspectroscopic imaging tools are established, automized data evaluation, i.e. image pattern recognition and automized image classification, of nonlinear optical images still bares great possibilities for future developments towards an objective clinical diagnosis. This contribution details the capability of nonlinear microscopy for both 3D visualization of human tissues and automated discrimination between healthy and diseased patterns using ex-vivo human skin samples. By means of CARS image alignment we show how to obtain a quasi-3D model of a skin biopsy, which allows us to trace the tissue structure in different projections. Furthermore, the potential of automated pattern and organization recognition to distinguish between healthy and keloidal skin tissue is discussed. A first classification algorithm employs the intrinsic geometrical features of collagen, which can be efficiently visualized by SHG microscopy. The shape of the collagen pattern allows conclusions about the physiological state of the skin, as the typical wavy collagen structure of healthy skin is disturbed e.g. in keloid formation. Based on the different collagen patterns a quantitative score characterizing the collagen waviness - and hence reflecting the physiological state of the tissue - is obtained. Further, two additional scoring methods for collagen organization, respectively based on a statistical analysis of the mutual organization of fibers and on FFT, are presented.

  14. Object recognition and localization from 3D point clouds by maximum-likelihood estimation.

    PubMed

    Dantanarayana, Harshana G; Huntley, Jonathan M

    2017-08-01

    We present an algorithm based on maximum-likelihood analysis for the automated recognition of objects, and estimation of their pose, from 3D point clouds. Surfaces segmented from depth images are used as the features, unlike 'interest point'-based algorithms which normally discard such data. Compared to the 6D Hough transform, it has negligible memory requirements, and is computationally efficient compared to iterative closest point algorithms. The same method is applicable to both the initial recognition/pose estimation problem as well as subsequent pose refinement through appropriate choice of the dispersion of the probability density functions. This single unified approach therefore avoids the usual requirement for different algorithms for these two tasks. In addition to the theoretical description, a simple 2 degrees of freedom (d.f.) example is given, followed by a full 6 d.f. analysis of 3D point cloud data from a cluttered scene acquired by a projected fringe-based scanner, which demonstrated an RMS alignment error as low as 0.3 mm.

  15. Human race as indicator of 3D planning of soft tissue of face and multidisciplinary approach.

    PubMed

    Nadazdyova, A; Samohyl, M; Stefankova, E; Pintesova, S; Stanko, P

    2017-01-01

    The aim of this study was to determine the optimal parameters for 3D soft tissue planning for ortognatic treatment by gender and increases the effectiveness of multidisciplinary cooperation. Craniofacial parameters which were analysed: nose breadth (al-al), bi-entocanthion breadth (en-en), bi-zygomatic breadth (zy-zy), bi-gonial breadth (go-go), total facial height (n-gn), mouth breadth (ch-ch), morphologic face height (sn-gn), upper-lip height (Ls-Stm), lower-lip height (Stm-Li) and pupils - mid-face (right). The statistically significant level was determined at p values < 0.05. We have determined the optimal parameters of chosen proportions for men and women as the common goal for ortodontist and maxilofacial surgeon. The gender and age influenced the variability of following parameters: bi-gonial breadth, total facial height and morphologic face height. The soft tissue values for craniofacial parameters can be used to identify the surgical-orthodontic goal for patient - europoid race. Due to the immigration and the mix of races it is necessary to take this fact into account (Tab. 3, Fig. 1, Ref. 41).

  16. Combining depth and gray images for fast 3D object recognition

    NASA Astrophysics Data System (ADS)

    Pan, Wang; Zhu, Feng; Hao, Yingming

    2016-10-01

    Reliable and stable visual perception systems are needed for humanoid robotic assistants to perform complex grasping and manipulation tasks. The recognition of the object and its precise 6D pose are required. This paper addresses the challenge of detecting and positioning a textureless known object, by estimating its complete 6D pose in cluttered scenes. A 3D perception system is proposed in this paper, which can robustly recognize CAD models in cluttered scenes for the purpose of grasping with a mobile manipulator. Our approach uses a powerful combination of two different camera technologies, Time-Of-Flight (TOF) and RGB, to segment the scene and extract objects. Combining the depth image and gray image to recognize instances of a 3D object in the world and estimate their 3D poses. The full pose estimation process is based on depth images segmentation and an efficient shape-based matching. At first, the depth image is used to separate the supporting plane of objects from the cluttered background. Thus, cluttered backgrounds are circumvented and the search space is extremely reduced. And a hierarchical model based on the geometry information of a priori CAD model of the object is generated in the offline stage. Then using the hierarchical model we perform a shape-based matching in 2D gray images. Finally, we validate the proposed method in a number of experiments. The results show that utilizing depth and gray images together can reach the demand of a time-critical application and reduce the error rate of object recognition significantly.

  17. Prevalence of face recognition deficits in middle childhood.

    PubMed

    Bennetts, Rachel J; Murray, Ebony; Boyce, Tian; Bate, Sarah

    2017-02-01

    Approximately 2-2.5% of the adult population is believed to show severe difficulties with face recognition, in the absence of any neurological injury-a condition known as developmental prosopagnosia (DP). However, to date no research has attempted to estimate the prevalence of face recognition deficits in children, possibly because there are very few child-friendly, well-validated tests of face recognition. In the current study, we examined face and object recognition in a group of primary school children (aged 5-11 years), to establish whether our tests were suitable for children and to provide an estimate of face recognition difficulties in children. In Experiment 1 (n = 184), children completed a pre-existing test of child face memory, the Cambridge Face Memory Test-Kids (CFMT-K), and a bicycle test with the same format. In Experiment 2 (n = 413), children completed three-alternative forced-choice matching tasks with faces and bicycles. All tests showed good psychometric properties. The face and bicycle tests were well matched for difficulty and showed a similar developmental trajectory. Neither the memory nor the matching tests were suitable to detect impairments in the youngest groups of children, but both tests appear suitable to screen for face recognition problems in middle childhood. In the current sample, 1.2-5.2% of children showed difficulties with face recognition; 1.2-4% showed face-specific difficulties-that is, poor face recognition with typical object recognition abilities. This is somewhat higher than previous adult estimates: It is possible that face matching tests overestimate the prevalence of face recognition difficulties in children; alternatively, some children may "outgrow" face recognition difficulties.

  18. [Face recognition in patients with autism spectrum disorders].

    PubMed

    Kita, Yosuke; Inagaki, Masumi

    2012-07-01

    The present study aimed to review previous research conducted on face recognition in patients with autism spectrum disorders (ASD). Face recognition is a key question in the ASD research field because it can provide clues for elucidating the neural substrates responsible for the social impairment of these patients. Historically, behavioral studies have reported low performance and/or unique strategies of face recognition among ASD patients. However, the performance and strategy of ASD patients is comparable to those of the control group, depending on the experimental situation or developmental stage, suggesting that face recognition of ASD patients is not entirely impaired. Recent brain function studies, including event-related potential and functional magnetic resonance imaging studies, have investigated the cognitive process of face recognition in ASD patients, and revealed impaired function in the brain's neural network comprising the fusiform gyrus and amygdala. This impaired function is potentially involved in the diminished preference for faces, and in the atypical development of face recognition, eliciting symptoms of unstable behavioral characteristics in these patients. Additionally, face recognition in ASD patients is examined from a different perspective, namely self-face recognition, and facial emotion recognition. While the former topic is intimately linked to basic social abilities such as self-other discrimination, the latter is closely associated with mentalizing. Further research on face recognition in ASD patients should investigate the connection between behavioral and neurological specifics in these patients, by considering developmental changes and the spectrum clinical condition of ASD.

  19. Heterogeneous face recognition using kernel prototype similarities.

    PubMed

    Klare, Brendan F; Jain, Anil K

    2013-06-01

    Heterogeneous face recognition (HFR) involves matching two face images from alternate imaging modalities, such as an infrared image to a photograph or a sketch to a photograph. Accurate HFR systems are of great value in various applications (e.g., forensics and surveillance), where the gallery databases are populated with photographs (e.g., mug shot or passport photographs) but the probe images are often limited to some alternate modality. A generic HFR framework is proposed in which both probe and gallery images are represented in terms of nonlinear similarities to a collection of prototype face images. The prototype subjects (i.e., the training set) have an image in each modality (probe and gallery), and the similarity of an image is measured against the prototype images from the corresponding modality. The accuracy of this nonlinear prototype representation is improved by projecting the features into a linear discriminant subspace. Random sampling is introduced into the HFR framework to better handle challenges arising from the small sample size problem. The merits of the proposed approach, called prototype random subspace (P-RS), are demonstrated on four different heterogeneous scenarios: 1) near infrared (NIR) to photograph, 2) thermal to photograph, 3) viewed sketch to photograph, and 4) forensic sketch to photograph.

  20. Flow control on a 3D backward facing ramp by pulsed jets

    NASA Astrophysics Data System (ADS)

    Joseph, Pierric; Bortolus, Dorian; Grasso, Francesco

    2014-06-01

    This paper presents an experimental study of flow separation control over a 3D backward facing ramp by means of pulsed jets. Such geometry has been selected to reproduce flow phenomena of interest for the automotive industry. The base flow has been characterised using PIV and pressure measurements. The results show that the classical notchback topology is correctly reproduced. A control system based on magnetic valves has been used to produce the pulsed jets whose properties have been characterised by hot wire anemometry. In order to shed some light on the role of the different parameters affecting the suppression of the slant recirculation area, a parametric study has been carried out by varying the frequency and the momentum coefficient of the jets for several Reynolds numbers. xml:lang="fr"

  1. Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy.

    PubMed

    Wernitznig, Stefan; Sele, Mariella; Urschler, Martin; Zankel, Armin; Pölt, Peter; Rind, F Claire; Leitinger, Gerd

    2016-05-01

    Elucidating the anatomy of neuronal circuits and localizing the synaptic connections between neurons, can give us important insights in how the neuronal circuits work. We are using serial block-face scanning electron microscopy (SBEM) to investigate the anatomy of a collision detection circuit including the Lobula Giant Movement Detector (LGMD) neuron in the locust, Locusta migratoria. For this, thousands of serial electron micrographs are produced that allow us to trace the neuronal branching pattern. The reconstruction of neurons was previously done manually by drawing cell outlines of each cell in each image separately. This approach was very time consuming and troublesome. To make the process more efficient a new interactive software was developed. It uses the contrast between the neuron under investigation and its surrounding for semi-automatic segmentation. For segmentation the user sets starting regions manually and the algorithm automatically selects a volume within the neuron until the edges corresponding to the neuronal outline are reached. Internally the algorithm optimizes a 3D active contour segmentation model formulated as a cost function taking the SEM image edges into account. This reduced the reconstruction time, while staying close to the manual reference segmentation result. Our algorithm is easy to use for a fast segmentation process, unlike previous methods it does not require image training nor an extended computing capacity. Our semi-automatic segmentation algorithm led to a dramatic reduction in processing time for the 3D-reconstruction of identified neurons. Copyright © 2016 Elsevier B.V. All rights reserved.

  2. Combining scale-space and similarity-based aspect graphs for fast 3D object recognition.

    PubMed

    Ulrich, Markus; Wiedemann, Christian; Steger, Carsten

    2012-10-01

    This paper describes an approach for recognizing instances of a 3D object in a single camera image and for determining their 3D poses. A hierarchical model is generated solely based on the geometry information of a 3D CAD model of the object. The approach does not rely on texture or reflectance information of the object's surface, making it useful for a wide range of industrial and robotic applications, e.g., bin-picking. A hierarchical view-based approach that addresses typical problems of previous methods is applied: It handles true perspective, is robust to noise, occlusions, and clutter to an extent that is sufficient for many practical applications, and is invariant to contrast changes. For the generation of this hierarchical model, a new model image generation technique by which scale-space effects can be taken into account is presented. The necessary object views are derived using a similarity-based aspect graph. The high robustness of an exhaustive search is combined with an efficient hierarchical search. The 3D pose is refined by using a least-squares adjustment that minimizes geometric distances in the image, yielding a position accuracy of up to 0.12 percent with respect to the object distance, and an orientation accuracy of up to 0.35 degree in our tests. The recognition time is largely independent of the complexity of the object, but depends mainly on the range of poses within which the object may appear in front of the camera. For efficiency reasons, the approach allows the restriction of the pose range depending on the application. Typical runtimes are in the range of a few hundred ms.

  3. Direct Gaze Modulates Face Recognition in Young Infants

    ERIC Educational Resources Information Center

    Farroni, Teresa; Massaccesi, Stefano; Menon, Enrica; Johnson, Mark H.

    2007-01-01

    From birth, infants prefer to look at faces that engage them in direct eye contact. In adults, direct gaze is known to modulate the processing of faces, including the recognition of individuals. In the present study, we investigate whether direction of gaze has any effect on face recognition in four-month-old infants. Four-month infants were shown…

  4. Street curb recognition in 3d point cloud data using morphological operations

    NASA Astrophysics Data System (ADS)

    Rodríguez-Cuenca, Borja; Concepción Alonso-Rodríguez, María; García-Cortés, Silverio; Ordóñez, Celestino

    2015-04-01

    Accurate and automatic detection of cartographic-entities saves a great deal of time and money when creating and updating cartographic databases. The current trend in remote sensing feature extraction is to develop methods that are as automatic as possible. The aim is to develop algorithms that can obtain accurate results with the least possible human intervention in the process. Non-manual curb detection is an important issue in road maintenance, 3D urban modeling, and autonomous navigation fields. This paper is focused on the semi-automatic recognition of curbs and street boundaries using a 3D point cloud registered by a mobile laser scanner (MLS) system. This work is divided into four steps. First, a coordinate system transformation is carried out, moving from a global coordinate system to a local one. After that and in order to simplify the calculations involved in the procedure, a rasterization based on the projection of the measured point cloud on the XY plane was carried out, passing from the 3D original data to a 2D image. To determine the location of curbs in the image, different image processing techniques such as thresholding and morphological operations were applied. Finally, the upper and lower edges of curbs are detected by an unsupervised classification algorithm on the curvature and roughness of the points that represent curbs. The proposed method is valid in both straight and curved road sections and applicable both to laser scanner and stereo vision 3D data due to the independence of its scanning geometry. This method has been successfully tested with two datasets measured by different sensors. The first dataset corresponds to a point cloud measured by a TOPCON sensor in the Spanish town of Cudillero. That point cloud comprises more than 6,000,000 points and covers a 400-meter street. The second dataset corresponds to a point cloud measured by a RIEGL sensor in the Austrian town of Horn. That point cloud comprises 8,000,000 points and represents a

  5. A new selective developmental deficit: Impaired object recognition with normal face recognition.

    PubMed

    Germine, Laura; Cashdollar, Nathan; Düzel, Emrah; Duchaine, Bradley

    2011-05-01

    Studies of developmental deficits in face recognition, or developmental prosopagnosia, have shown that individuals who have not suffered brain damage can show face recognition impairments coupled with normal object recognition (Duchaine and Nakayama, 2005; Duchaine et al., 2006; Nunn et al., 2001). However, no developmental cases with the opposite dissociation - normal face recognition with impaired object recognition - have been reported. The existence of a case of non-face developmental visual agnosia would indicate that the development of normal face recognition mechanisms does not rely on the development of normal object recognition mechanisms. To see whether a developmental variant of non-face visual object agnosia exists, we conducted a series of web-based object and face recognition tests to screen for individuals showing object recognition memory impairments but not face recognition impairments. Through this screening process, we identified AW, an otherwise normal 19-year-old female, who was then tested in the lab on face and object recognition tests. AW's performance was impaired in within-class visual recognition memory across six different visual categories (guns, horses, scenes, tools, doors, and cars). In contrast, she scored normally on seven tests of face recognition, tests of memory for two other object categories (houses and glasses), and tests of recall memory for visual shapes. Testing confirmed that her impairment was not related to a general deficit in lower-level perception, object perception, basic-level recognition, or memory. AW's results provide the first neuropsychological evidence that recognition memory for non-face visual object categories can be selectively impaired in individuals without brain damage or other memory impairment. These results indicate that the development of recognition memory for faces does not depend on intact object recognition memory and provide further evidence for category-specific dissociations in visual

  6. Evaluation of the Accuracy, Reliability, and Reproducibility of Two Different 3D Face-Scanning Systems.

    PubMed

    Ye, Hongqiang; Lv, Longwei; Liu, Yunsong; Liu, Yushu; Zhou, Yongsheng

    2016-01-01

    To compare the accuracy, reliability, and reproducibility of a structured light scanning system and a stereophotogrammetry scanning system on human faces. A total of 10 healthy volunteers were included in this study. After marking of facial anatomy points, their faces were scanned by a structured light scanning system and a stereophotogrammetry system, and three-dimensional (3D) images were reconstructed with corresponding software. For each volunteer, scanning was performed twice after calibration. Linear measurements were calculated and compared for the two scanning techniques with direct caliper measurements. Absolute errors (AE), absolute percentage errors (APE), and intraclass correlation coefficients (ICC) were chosen as indices to determine the accuracy, reliability, and reproducibility of the two systems. There was no statistically significant difference among the three measuring techniques (.891 < P < .999). Both scanning systems demonstrated high accuracy (AE = 0.58 ± 0.37 mm and APE = 1.11 ± 0.73% for the structured light system; AE = 0.62 ± 0.39 mm and APE 1.17 ± 0.71% for the stereophotogrammetry system). The two systems demonstrated extremely high reliability compared to caliper measurement (0.982 < ICC < 0.998 for the structured light system; 0.984 < ICC < 0.999 for the stereophotogrammetry system). In addition, high reproducibility was observed with the two systems (0.981 < ICC < 0.999 for the structured light system; 0.984 < ICC < 1.000 for the stereophotogrammetry system). When applied in scanning and measuring human faces, the structured light scanning system and stereophotogrammetry scanning system both demonstrated high accuracy, reliability, and reproducibility.

  7. Fusion of 3D laser scanner and depth images for obstacle recognition in mobile applications

    NASA Astrophysics Data System (ADS)

    Budzan, Sebastian; Kasprzyk, Jerzy

    2016-02-01

    The problem of obstacle detection and recognition or, generally, scene mapping is one of the most investigated problems in computer vision, especially in mobile applications. In this paper a fused optical system using depth information with color images gathered from the Microsoft Kinect sensor and 3D laser range scanner data is proposed for obstacle detection and ground estimation in real-time mobile systems. The algorithm consists of feature extraction in the laser range images, processing of the depth information from the Kinect sensor, fusion of the sensor information, and classification of the data into two separate categories: road and obstacle. Exemplary results are presented and it is shown that fusion of information gathered from different sources increases the effectiveness of the obstacle detection in different scenarios, and it can be used successfully for road surface mapping.

  8. Analyzing the relevance of shape descriptors in automated recognition of facial gestures in 3D images

    NASA Astrophysics Data System (ADS)

    Rodriguez A., Julian S.; Prieto, Flavio

    2013-03-01

    The present document shows and explains the results from analyzing shape descriptors (DESIRE and Spherical Spin Image) for facial recognition of 3D images. DESIRE is a descriptor made of depth images, silhouettes and rays extended from a polygonal mesh; whereas the Spherical Spin Image (SSI) associated to a polygonal mesh point, is a 2D histogram built from neighboring points by using the position information that captures features of the local shape. The database used contains images of facial expressions which in average were recognized 88.16% using a neuronal network and 91.11% with a Bayesian classifier in the case of the first descriptor; in contrast, the second descriptor only recognizes in average 32% and 23,6% using the same mentioned classifiers respectively.

  9. An optimal sensing strategy for recognition and localization of 3-D natural quadric objects

    NASA Technical Reports Server (NTRS)

    Lee, Sukhan; Hahn, Hernsoo

    1991-01-01

    An optimal sensing strategy for an optical proximity sensor system engaged in the recognition and localization of 3-D natural quadric objects is presented. The optimal sensing strategy consists of the selection of an optimal beam orientation and the determination of an optimal probing plane that compose an optimal data collection operation known as an optimal probing. The decision of an optimal probing is based on the measure of discrimination power of a cluster of surfaces on a multiple interpretation image (MII), where the measure of discrimination power is defined in terms of a utility function computing the expected number of interpretations that can be pruned out by a probing. An object representation suitable for active sensing based on a surface description vector (SDV) distribution graph and hierarchical tables is presented. Experimental results are shown.

  10. Structured light 3D depth map enhancement and gesture recognition using image content adaptive filtering

    NASA Astrophysics Data System (ADS)

    Ramachandra, Vikas; Nash, James; Atanassov, Kalin; Goma, Sergio

    2013-03-01

    A structured-light system for depth estimation is a type of 3D active sensor that consists of a structured-light projector that projects an illumination pattern on the scene (e.g. mask with vertical stripes) and a camera which captures the illuminated scene. Based on the received patterns, depths of different regions in the scene can be inferred. In this paper, we use side information in the form of image structure to enhance the depth map. This side information is obtained from the received light pattern image reflected by the scene itself. The processing steps run real time. This post-processing stage in the form of depth map enhancement can be used for better hand gesture recognition, as is illustrated in this paper.

  11. Image preprocessing study on KPCA-based face recognition

    NASA Astrophysics Data System (ADS)

    Li, Xuan; Li, Dehua

    2015-12-01

    Face recognition as an important biometric identification method, with its friendly, natural, convenient advantages, has obtained more and more attention. This paper intends to research a face recognition system including face detection, feature extraction and face recognition, mainly through researching on related theory and the key technology of various preprocessing methods in face detection process, using KPCA method, focuses on the different recognition results in different preprocessing methods. In this paper, we choose YCbCr color space for skin segmentation and choose integral projection for face location. We use erosion and dilation of the opening and closing operation and illumination compensation method to preprocess face images, and then use the face recognition method based on kernel principal component analysis method for analysis and research, and the experiments were carried out using the typical face database. The algorithms experiment on MATLAB platform. Experimental results show that integration of the kernel method based on PCA algorithm under certain conditions make the extracted features represent the original image information better for using nonlinear feature extraction method, which can obtain higher recognition rate. In the image preprocessing stage, we found that images under various operations may appear different results, so as to obtain different recognition rate in recognition stage. At the same time, in the process of the kernel principal component analysis, the value of the power of the polynomial function can affect the recognition result.

  12. Developement of 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)

    SciTech Connect

    Deputch, G.; Hoff, J.; Lipton, R.; Liu, T.; Olsen, J.; Ramberg, E.; Wu, Jin-Yuan; Yarema, R.; Shochet, M.; Tang, F.; Demarteau, M.; /Argonne /INFN, Padova

    2011-04-13

    Many next-generation physics experiments will be characterized by the collection of large quantities of data, taken in rapid succession, from which scientists will have to unravel the underlying physical processes. In most cases, large backgrounds will overwhelm the physics signal. Since the quantity of data that can be stored for later analysis is limited, real-time event selection is imperative to retain the interesting events while rejecting the background. Scaling of current technologies is unlikely to satisfy the scientific needs of future projects, so investments in transformational new technologies need to be made. For example, future particle physics experiments looking for rare processes will have to address the demanding challenges of fast pattern recognition in triggering as detector hit density becomes significantly higher due to the high luminosity required to produce the rare processes. In this proposal, we intend to develop hardware-based technology that significantly advances the state-of-the-art for fast pattern recognition within and outside HEP using the 3D vertical integration technology that has emerged recently in industry. The ultimate physics reach of the LHC experiments will crucially depend on the tracking trigger's ability to help discriminate between interesting rare events and the background. Hardware-based pattern recognition for fast triggering on particle tracks has been successfully used in high-energy physics experiments for some time. The CDF Silicon Vertex Trigger (SVT) at the Fermilab Tevatron is an excellent example. The method used there, developed in the 1990's, is based on algorithms that use a massively parallel associative memory architecture to identify patterns efficiently at high speed. However, due to much higher occupancy and event rates at the LHC, and the fact that the LHC detectors have a much larger number of channels in their tracking detectors, there is an enormous challenge in implementing pattern recognition

  13. Familiar Person Recognition: Is Autonoetic Consciousness More Likely to Accompany Face Recognition Than Voice Recognition?

    NASA Astrophysics Data System (ADS)

    Barsics, Catherine; Brédart, Serge

    2010-11-01

    Autonoetic consciousness is a fundamental property of human memory, enabling us to experience mental time travel, to recollect past events with a feeling of self-involvement, and to project ourselves in the future. Autonoetic consciousness is a characteristic of episodic memory. By contrast, awareness of the past associated with a mere feeling of familiarity or knowing relies on noetic consciousness, depending on semantic memory integrity. Present research was aimed at evaluating whether conscious recollection of episodic memories is more likely to occur following the recognition of a familiar face than following the recognition of a familiar voice. Recall of semantic information (biographical information) was also assessed. Previous studies that investigated the recall of biographical information following person recognition used faces and voices of famous people as stimuli. In this study, the participants were presented with personally familiar people's voices and faces, thus avoiding the presence of identity cues in the spoken extracts and allowing a stricter control of frequency exposure with both types of stimuli (voices and faces). In the present study, the rate of retrieved episodic memories, associated with autonoetic awareness, was significantly higher from familiar faces than familiar voices even though the level of overall recognition was similar for both these stimuli domains. The same pattern was observed regarding semantic information retrieval. These results and their implications for current Interactive Activation and Competition person recognition models are discussed.

  14. Interim results from a neural network 3-D automatic target recognition program

    NASA Astrophysics Data System (ADS)

    Thoet, William; Rainey, Timothy G.; Slutz, Lee A.; Weingard, Fred

    1992-09-01

    Recent results from the Artificial Neural VIsion Learning (ANVIL) program are presented. The focus of the ANVIL program is to apply neural network technologies to the air-to-surface 3D automatic target recognition (ATR) problem. The 3D Multiple Object Detection and Location System (MODALS) neural network was developed under the ANVIL program to simultaneously detect, locate, segment, and identify multiple targets. The performance results show a very high identification accuracy, a high detection rate, and low false alarm rate, even for areas with high clutter and shadowing. The results are shown as detection/false alarm curves and identification/false alarm curves. In addition, positional detection accuracy is shown for various scale sizes. To provide data for the program, visible terrain board imagery was collected under a variety of background and lighting conditions. Tests were made on over 500 targets of five types and two classes. These targets varied in scale by up to -25%, varied in azimuth by up to 120 degrees, and varied in elevation by up to 10 degrees. The performance results are shown for targets with resolution ranging from 9 to 700 pixels on target. This work is being performed under contract to Wright Laboratory AAAT-1.

  15. Fast and flexible 3D object recognition solutions for machine vision applications

    NASA Astrophysics Data System (ADS)

    Effenberger, Ira; Kühnle, Jens; Verl, Alexander

    2013-03-01

    In automation and handling engineering, supplying work pieces between different stages along the production process chain is of special interest. Often the parts are stored unordered in bins or lattice boxes and hence have to be separated and ordered for feeding purposes. An alternative to complex and spacious mechanical systems such as bowl feeders or conveyor belts, which are typically adapted to the parts' geometry, is using a robot to grip the work pieces out of a bin or from a belt. Such applications are in need of reliable and precise computer-aided object detection and localization systems. For a restricted range of parts, there exists a variety of 2D image processing algorithms that solve the recognition problem. However, these methods are often not well suited for the localization of randomly stored parts. In this paper we present a fast and flexible 3D object recognizer that localizes objects by identifying primitive features within the objects. Since technical work pieces typically consist to a substantial degree of geometric primitives such as planes, cylinders and cones, such features usually carry enough information in order to determine the position of the entire object. Our algorithms use 3D best-fitting combined with an intelligent data pre-processing step. The capability and performance of this approach is shown by applying the algorithms to real data sets of different industrial test parts in a prototypical bin picking demonstration system.

  16. Face age and sex modulate the other-race effect in face recognition.

    PubMed

    Wallis, Jennifer; Lipp, Ottmar V; Vanman, Eric J

    2012-11-01

    Faces convey a variety of socially relevant cues that have been shown to affect recognition, such as age, sex, and race, but few studies have examined the interactive effect of these cues. White participants of two distinct age groups were presented with faces that differed in race, age, and sex in a face recognition paradigm. Replicating the other-race effect, young participants recognized young own-race faces better than young other-race faces. However, recognition performance did not differ across old faces of different races (Experiments 1, 2A). In addition, participants showed an other-age effect, recognizing White young faces better than White old faces. Sex affected recognition performance only when age was not varied (Experiment 2B). Overall, older participants showed a similar recognition pattern (Experiment 3) as young participants, displaying an other-race effect for young, but not old, faces. However, they recognized young and old White faces on a similar level. These findings indicate that face cues interact to affect recognition performance such that age and sex information reliably modulate the effect of race cues. These results extend accounts of face recognition that explain recognition biases (such as the other-race effect) as a function of dichotomous ingroup/outgroup categorization, in that outgroup characteristics are not simply additive but interactively determine recognition performance.

  17. Covert face recognition relies on affective valence in congenital prosopagnosia.

    PubMed

    Bate, Sarah; Haslam, Catherine; Jansari, Ashok; Hodgson, Timothy L

    2009-06-01

    Dominant accounts of covert recognition in prosopagnosia assume subthreshold activation of face representations created prior to onset of the disorder. Yet, such accounts cannot explain covert recognition in congenital prosopagnosia, where the impairment is present from birth. Alternatively, covert recognition may rely on affective valence, yet no study has explored this possibility. The current study addressed this issue in 3 individuals with congenital prosopagnosia, using measures of the scanpath to indicate recognition. Participants were asked to memorize 30 faces paired with descriptions of aggressive, nice, or neutral behaviours. In a later recognition test, eye movements were monitored while participants discriminated studied from novel faces. Sampling was reduced for studied--nice compared to studied--aggressive faces, and performance for studied--neutral and novel faces fell between these two conditions. This pattern of findings suggests that (a) positive emotion can facilitate processing in prosopagnosia, and (b) covert recognition may rely on emotional valence rather than familiarity.

  18. 3D Object Recognition of a Robotic Navigation Aid for the Visually Impaired.

    PubMed

    Ye, Cang; Qian, Xiangfei

    2017-09-01

    This paper presents a 3D object recognition method and its implementation on a Robotic Navigation Aid (RNA) to allow real-time detection of indoor structural objects for the navigation of a blind person. The method segments a point cloud into numerous planar patches and extracts their Inter-Plane Relationships (IPRs). Based on the existing IPRs of the object models, the method defines 6 High Level Features (HLFs) and determines the HLFs for each patch. A Gaussian-Mixture-Model-based plane classifier is then devised to classify each planar patch into one belonging to a particular object model. Finally, a recursive plane clustering procedure is used to cluster the classified planes into the model objects. As the proposed method uses geometric context to detect an object, it is robust to the object's visual appearance change. As a result, it is ideal for detecting structural objects (e.g., stairways, doorways, etc.). In addition, it has high scalability and parallelism. The method is also capable of detecting some indoor non-structural objects. Experimental results demonstrate that the proposed method has a high success rate in object recognition.

  19. On Integral Invariants for Effective 3-D Motion Trajectory Matching and Recognition.

    PubMed

    Shao, Zhanpeng; Li, Youfu

    2016-02-01

    Motion trajectories tracked from the motions of human, robots, and moving objects can provide an important clue for motion analysis, classification, and recognition. This paper defines some new integral invariants for a 3-D motion trajectory. Based on two typical kernel functions, we design two integral invariants, the distance and area integral invariants. The area integral invariants are estimated based on the blurred segment of noisy discrete curve to avoid the computation of high-order derivatives. Such integral invariants for a motion trajectory enjoy some desirable properties, such as computational locality, uniqueness of representation, and noise insensitivity. Moreover, our formulation allows the analysis of motion trajectories at a range of scales by varying the scale of kernel function. The features of motion trajectories can thus be perceived at multiscale levels in a coarse-to-fine manner. Finally, we define a distance function to measure the trajectory similarity to find similar trajectories. Through the experiments, we examine the robustness and effectiveness of the proposed integral invariants and find that they can capture the motion cues in trajectory matching and sign recognition satisfactorily.

  20. A multistep approach for infrared face recognition in texture space

    NASA Astrophysics Data System (ADS)

    Akhloufi, Moulay A.; Bendada, Abdelhakim

    2013-05-01

    Face recognition is an area of computer vision that has attracted a lot of interest from the research community. A growing demand for robust face recognition in security applications has driven interesting advancements in this field. In this work, we introduce a new multistep approach for face recognition in the infrared spectrum. The proposed approach works in texture space using binary and ternary pattern descriptors. The approach operates in two steps. In the first step, dimensionality reduction techniques are used to classify the preprocessed infrared face image. This operation permits the selection of the highest score candidates. In the second step, a small set of these candidates are then classified using a correlation based approach. This last step permits the selection of the best matching candidate. The obtained results show a high increase in the face recognition performance when a multistep approach is used compared to dimensionality reduction face recognition techniques alone.

  1. Illumination invariant face recognition using near-infrared images.

    PubMed

    Li, Stan Z; Chu, Rufeng; Liao, Shengcai; Zhang, Lun

    2007-04-01

    Most current face recognition systems are designed for indoor, cooperative-user applications. However, even in thus-constrained applications, most existing systems, academic and commercial, are compromised in accuracy by changes in environmental illumination. In this paper, we present a novel solution for illumination invariant face recognition for indoor, cooperative-user applications. First, we present an active near infrared (NIR) imaging system that is able to produce face images of good condition regardless of visible lights in the environment. Second, we show that the resulting face images encode intrinsic information of the face, subject only to a monotonic transform in the gray tone; based on this, we use local binary pattern (LBP) features to compensate for the monotonic transform, thus deriving an illumination invariant face representation. Then, we present methods for face recognition using NIR images; statistical learning algorithms are used to extract most discriminative features from a large pool of invariant LBP features and construct a highly accurate face matching engine. Finally, we present a system that is able to achieve accurate and fast face recognition in practice, in which a method is provided to deal with specular reflections of active NIR lights on eyeglasses, a critical issue in active NIR image-based face recognition. Extensive, comparative results are provided to evaluate the imaging hardware, the face and eye detection algorithms, and the face recognition algorithms and systems, with respect to various factors, including illumination, eyeglasses, time lapse, and ethnic groups.

  2. Wavelet filtered shifted phase-encoded joint transform correlation for face recognition

    NASA Astrophysics Data System (ADS)

    Moniruzzaman, Md.; Alam, Mohammad S.

    2017-05-01

    A new wavelet-filtered-based Shifted- phase-encoded Joint Transform Correlation (WPJTC) technique has been proposed for efficient face recognition. The proposed technique uses discrete wavelet decomposition for preprocessing and can effectively accommodate various 3D facial distortions, effects of noise, and illumination variations. After analyzing different forms of wavelet basis functions, an optimal method has been proposed by considering the discrimination capability and processing speed as performance trade-offs. The proposed technique yields better correlation discrimination compared to alternate pattern recognition techniques such as phase-shifted phase-encoded fringe-adjusted joint transform correlator. The performance of the proposed WPJTC has been tested using the Yale facial database and extended Yale facial database under different environments such as illumination variation, noise, and 3D changes in facial expressions. Test results show that the proposed WPJTC yields better performance compared to alternate JTC based face recognition techniques.

  3. Knowledge Based 3d Building Model Recognition Using Convolutional Neural Networks from LIDAR and Aerial Imageries

    NASA Astrophysics Data System (ADS)

    Alidoost, F.; Arefi, H.

    2016-06-01

    In recent years, with the development of the high resolution data acquisition technologies, many different approaches and algorithms have been presented to extract the accurate and timely updated 3D models of buildings as a key element of city structures for numerous applications in urban mapping. In this paper, a novel and model-based approach is proposed for automatic recognition of buildings' roof models such as flat, gable, hip, and pyramid hip roof models based on deep structures for hierarchical learning of features that are extracted from both LiDAR and aerial ortho-photos. The main steps of this approach include building segmentation, feature extraction and learning, and finally building roof labeling in a supervised pre-trained Convolutional Neural Network (CNN) framework to have an automatic recognition system for various types of buildings over an urban area. In this framework, the height information provides invariant geometric features for convolutional neural network to localize the boundary of each individual roofs. CNN is a kind of feed-forward neural network with the multilayer perceptron concept which consists of a number of convolutional and subsampling layers in an adaptable structure and it is widely used in pattern recognition and object detection application. Since the training dataset is a small library of labeled models for different shapes of roofs, the computation time of learning can be decreased significantly using the pre-trained models. The experimental results highlight the effectiveness of the deep learning approach to detect and extract the pattern of buildings' roofs automatically considering the complementary nature of height and RGB information.

  4. Newborns' Face Recognition: Role of Inner and Outer Facial Features

    ERIC Educational Resources Information Center

    Turati, Chiara; Macchi Cassia, Viola; Simion, Francesca; Leo, Irene

    2006-01-01

    Existing data indicate that newborns are able to recognize individual faces, but little is known about what perceptual cues drive this ability. The current study showed that either the inner or outer features of the face can act as sufficient cues for newborns' face recognition (Experiment 1), but the outer part of the face enjoys an advantage…

  5. Neural Substrates for Episodic Encoding and Recognition of Unfamiliar Faces

    ERIC Educational Resources Information Center

    Hofer, Alex; Siedentopf, Christian M.; Ischebeck, Anja; Rettenbacher, Maria A.; Verius, Michael; Golaszewski, Stefan M.; Felber, Stephan; Fleischhacker, W. Wolfgang

    2007-01-01

    Functional MRI was used to investigate brain activation in healthy volunteers during encoding of unfamiliar faces as well as during correct recognition of newly learned faces (CR) compared to correct identification of distractor faces (CF), missed alarms (not recognizing previously presented faces, MA), and false alarms (incorrectly recognizing…

  6. Graph optimized Laplacian eigenmaps for face recognition

    NASA Astrophysics Data System (ADS)

    Dornaika, F.; Assoum, A.; Ruichek, Y.

    2015-01-01

    In recent years, a variety of nonlinear dimensionality reduction techniques (NLDR) have been proposed in the literature. They aim to address the limitations of traditional techniques such as PCA and classical scaling. Most of these techniques assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. They provide a mapping from the high-dimensional space to the low-dimensional embedding and may be viewed, in the context of machine learning, as a preliminary feature extraction step, after which pattern recognition algorithms are applied. Laplacian Eigenmaps (LE) is a nonlinear graph-based dimensionality reduction method. It has been successfully applied in many practical problems such as face recognition. However the construction of LE graph suffers, similarly to other graph-based DR techniques from the following issues: (1) the neighborhood graph is artificially defined in advance, and thus does not necessary benefit the desired DR task; (2) the graph is built using the nearest neighbor criterion which tends to work poorly due to the high-dimensionality of original space; and (3) its computation depends on two parameters whose values are generally uneasy to assign, the neighborhood size and the heat kernel parameter. To address the above-mentioned problems, for the particular case of the LPP method (a linear version of LE), L. Zhang et al.1 have developed a novel DR algorithm whose idea is to integrate graph construction with specific DR process into a unified framework. This algorithm results in an optimized graph rather than a predefined one.

  7. Face recognition across makeup and plastic surgery from real-world images

    NASA Astrophysics Data System (ADS)

    Moeini, Ali; Faez, Karim; Moeini, Hossein

    2015-09-01

    A study for feature extraction is proposed to handle the problem of facial appearance changes including facial makeup and plastic surgery in face recognition. To extend a face recognition method robust to facial appearance changes, features are individually extracted from facial depth on which facial makeup and plastic surgery have no effect. Then facial depth features are added to facial texture features to perform feature extraction. Accordingly, a three-dimensional (3-D) face is reconstructed from only a single two-dimensional (2-D) frontal image in real-world scenarios. Then the facial depth is extracted from the reconstructed model. Afterward, the dual-tree complex wavelet transform (DT-CWT) is applied to both texture and reconstructed depth images to extract the feature vectors. Finally, the final feature vectors are generated by combining 2-D and 3-D feature vectors, and are then classified by adopting the support vector machine. Promising results have been achieved for makeup-invariant face recognition on two available image databases including YouTube makeup and virtual makeup, and plastic surgery-invariant face recognition on a plastic surgery face database is compared to several state-of-the-art feature extraction methods. Several real-world scenarios are also planned to evaluate the performance of the proposed method on a combination of these three databases with 1102 subjects.

  8. Isolating the Special Component of Face Recognition: Peripheral Identification and a Mooney Face

    ERIC Educational Resources Information Center

    McKone, Elinor

    2004-01-01

    A previous finding argues that, for faces, configural (holistic) processing can operate even in the complete absence of part-based contributions to recognition. Here, this result is confirmed using 2 methods. In both, recognition of inverted faces (parts only) was removed altogether (chance identification of faces in the periphery; no perception…

  9. Isolating the Special Component of Face Recognition: Peripheral Identification and a Mooney Face

    ERIC Educational Resources Information Center

    McKone, Elinor

    2004-01-01

    A previous finding argues that, for faces, configural (holistic) processing can operate even in the complete absence of part-based contributions to recognition. Here, this result is confirmed using 2 methods. In both, recognition of inverted faces (parts only) was removed altogether (chance identification of faces in the periphery; no perception…

  10. Familiar Face Recognition in Children with Autism: The Differential Use of Inner and Outer Face Parts

    ERIC Educational Resources Information Center

    Wilson, Rebecca; Pascalis, Olivier; Blades, Mark

    2007-01-01

    We investigated whether children with autistic spectrum disorders (ASD) have a deficit in recognising familiar faces. Children with ASD were given a forced choice familiar face recognition task with three conditions: full faces, inner face parts and outer face parts. Control groups were children with developmental delay (DD) and typically…

  11. Transfer between Pose and Illumination Training in Face Recognition

    ERIC Educational Resources Information Center

    Liu, Chang Hong; Bhuiyan, Md. Al-Amin; Ward, James; Sui, Jie

    2009-01-01

    The relationship between pose and illumination learning in face recognition was examined in a yes-no recognition paradigm. The authors assessed whether pose training can transfer to a new illumination or vice versa. Results show that an extensive level of pose training through a face-name association task was able to generalize to a new…

  12. Recognition of Moving and Static Faces by Young Infants

    ERIC Educational Resources Information Center

    Otsuka, Yumiko; Konishi, Yukuo; Kanazawa, So; Yamaguchi, Masami K.; Abdi, Herve; O'Toole, Alice J.

    2009-01-01

    This study compared 3- to 4-month-olds' recognition of previously unfamiliar faces learned in a moving or a static condition. Infants in the moving condition showed successful recognition with only 30 s familiarization, even when different images of a face were used in the familiarization and test phase (Experiment 1). In contrast, infants in the…

  13. Hyperspectral face recognition with spatiospectral information fusion and PLS regression.

    PubMed

    Uzair, Muhammad; Mahmood, Arif; Mian, Ajmal

    2015-03-01

    Hyperspectral imaging offers new opportunities for face recognition via improved discrimination along the spectral dimension. However, it poses new challenges, including low signal-to-noise ratio, interband misalignment, and high data dimensionality. Due to these challenges, the literature on hyperspectral face recognition is not only sparse but is limited to ad hoc dimensionality reduction techniques and lacks comprehensive evaluation. We propose a hyperspectral face recognition algorithm using a spatiospectral covariance for band fusion and partial least square regression for classification. Moreover, we extend 13 existing face recognition techniques, for the first time, to perform hyperspectral face recognition.We formulate hyperspectral face recognition as an image-set classification problem and evaluate the performance of seven state-of-the-art image-set classification techniques. We also test six state-of-the-art grayscale and RGB (color) face recognition algorithms after applying fusion techniques on hyperspectral images. Comparison with the 13 extended and five existing hyperspectral face recognition techniques on three standard data sets show that the proposed algorithm outperforms all by a significant margin. Finally, we perform band selection experiments to find the most discriminative bands in the visible and near infrared response spectrum.

  14. Recognition of Moving and Static Faces by Young Infants

    ERIC Educational Resources Information Center

    Otsuka, Yumiko; Konishi, Yukuo; Kanazawa, So; Yamaguchi, Masami K.; Abdi, Herve; O'Toole, Alice J.

    2009-01-01

    This study compared 3- to 4-month-olds' recognition of previously unfamiliar faces learned in a moving or a static condition. Infants in the moving condition showed successful recognition with only 30 s familiarization, even when different images of a face were used in the familiarization and test phase (Experiment 1). In contrast, infants in the…

  15. When the face fits: recognition of celebrities from matching and mismatching faces and voices.

    PubMed

    Stevenage, Sarah V; Neil, Greg J; Hamlin, Iain

    2014-01-01

    The results of two experiments are presented in which participants engaged in a face-recognition or a voice-recognition task. The stimuli were face-voice pairs in which the face and voice were co-presented and were either "matched" (same person), "related" (two highly associated people), or "mismatched" (two unrelated people). Analysis in both experiments confirmed that accuracy and confidence in face recognition was consistently high regardless of the identity of the accompanying voice. However accuracy of voice recognition was increasingly affected as the relationship between voice and accompanying face declined. Moreover, when considering self-reported confidence in voice recognition, confidence remained high for correct responses despite the proportion of these responses declining across conditions. These results converged with existing evidence indicating the vulnerability of voice recognition as a relatively weak signaller of identity, and results are discussed in the context of a person-recognition framework.

  16. Rapid 3D measurement of human faces for biometric application by digital fringe projection with digital light projection (DLP®)

    NASA Astrophysics Data System (ADS)

    Benderoth, Christian; Bell, Rebecca L.; Frankowski, Gottfried

    2008-04-01

    Facial recognitions of people can be used for the identification of individuals, or can serve as verification e.g. for access controls. The process requires, that the facial data is captured and then compared with stored reference data. In this context, far better recognition performances can be expected from 3-dimensional facial recognition systems than can be from the 2-dimensional systems which are currently used. The accuracy with which the facial profile can be captured, depends on the speed off the measuring data acquisition i.e. the scanning speed and on the measuring accuracy of the measuring device i.e. the 3D scanner.

  17. Face and body recognition show similar improvement during childhood.

    PubMed

    Bank, Samantha; Rhodes, Gillian; Read, Ainsley; Jeffery, Linda

    2015-09-01

    Adults are proficient in extracting identity cues from faces. This proficiency develops slowly during childhood, with performance not reaching adult levels until adolescence. Bodies are similar to faces in that they convey identity cues and rely on specialized perceptual mechanisms. However, it is currently unclear whether body recognition mirrors the slow development of face recognition during childhood. Recent evidence suggests that body recognition develops faster than face recognition. Here we measured body and face recognition in 6- and 10-year-old children and adults to determine whether these two skills show different amounts of improvement during childhood. We found no evidence that they do. Face and body recognition showed similar improvement with age, and children, like adults, were better at recognizing faces than bodies. These results suggest that the mechanisms of face and body memory mature at a similar rate or that improvement of more general cognitive and perceptual skills underlies improvement of both face and body recognition. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Electrophysiological markers of covert face recognition in developmental prosopagnosia.

    PubMed

    Eimer, Martin; Gosling, Angela; Duchaine, Bradley

    2012-02-01

    To study the existence and neural basis of covert face recognition in individuals with developmental prosopagnosia, we tested a group of 12 participants with developmental prosopagnosia in a task that required them to judge the familiarity of successively presented famous or non-famous faces. Electroencephalography was recorded during task performance, and event-related brain potentials were computed for recognized famous faces, non-recognized famous faces and non-famous faces. In six individuals with developmental prosopagnosia, non-recognized famous faces triggered an occipito-temporal N250 component, which is thought to reflect the activation of stored visual memory traces of known individual faces. In contrast to the N250, the P600f component, which is linked to late semantic stages of face identity processing, was not triggered by non-recognized famous faces. Event-related potential correlates of explicit face recognition obtained on those few trials where participants with developmental prosopagnosia classified famous faces as known or familiar, were similar to the effects previously found in participants with intact face recognition abilities, suggesting that face recognition mechanisms in individuals with developmental prosopagnosia are not qualitatively different from that of unimpaired individuals. Overall, these event-related potential results provide the first neurophysiological evidence for covert face recognition in developmental prosopagnosia, and suggest this phenomenon results from disconnected links between intact identity-specific visual memory traces and later semantic face processing stages. They also imply that the activation of stored visual representations of familiar faces is not sufficient for conscious explicit face recognition.

  19. Three dimensional surface analyses of pubic symphyseal faces of contemporary Japanese reconstructed with 3D digitized scanner.

    PubMed

    Biwasaka, Hitoshi; Sato, Kei; Aoki, Yasuhiro; Kato, Hideaki; Maeno, Yoshitaka; Tanijiri, Toyohisa; Fujita, Sachiko; Dewa, Koji

    2013-09-01

    Three dimensional pubic bone images were analyzed to quantify some age-dependent morphological changes of the symphyseal faces of contemporary Japanese residents. The images were synthesized from 145 bone specimens with 3D measuring device. Phases of Suchey-Brooks system were determined on the 3D pubic symphyseal images without discrepancy from those carried out on the real bones because of the high fidelity. Subsequently, mean curvatures of the pubic symphyseal faces to examine concavo-convex condition of the surfaces were analyzed on the 3D images. Average values of absolute mean curvatures of phase 1 and 2 groups were higher than those of phase 3-6 ones, whereas the values were approximately constant over phase 3 presumably reflecting the inactivation of pubic faces over phase 3. Ratio of the concave areas increased gradually with progressing phase or age classes, although convex areas were predominant in every phase.

  20. Infrared face recognition based on multiwavelet transform and PCA

    NASA Astrophysics Data System (ADS)

    Li, Xiafang; Wang, Jianmin; Xie, Zhihua

    2012-10-01

    To extract the discriminative information from the sparse representation of infrared face, infrared face recognition method combining multiwavelet transform and principal component analysis (PCA) is proposed in this paper. Firstly, the effective information in infrared face is represented by multi-wavelet transformation. Then, to integrate more useful information to infrared face recognition, we assign the corresponding weights to different sub-bands in multi-wavelet domain. Finally, based on the weighted fusion distance, the 1-NN classifier is applied to get final recognition result. The experiment results show that the recognition performance of sparse representation based on multi-wavelet representation outperforms that of method based on usual wavelet representation; and the proposed infrared face method considering the useful information in different sub-bands of multiwavelet has better recognition performance, compared with the method based on approximate sub-band.

  1. Computational chemistry approach to protein kinase recognition using 3D stochastic van der Waals spectral moments.

    PubMed

    González-Díaz, Humberto; Saíz-Urra, Liane; Molina, Reinaldo; González-Díaz, Yenny; Sánchez-González, Angeles

    2007-04-30

    Three-dimensional (3D) protein structures now frequently lack functional annotations because of the increase in the rate at which chemical structures are solved with respect to experimental knowledge of biological activity. As a result, predicting structure-function relationships for proteins is an active research field in computational chemistry and has implications in medicinal chemistry, biochemistry and proteomics. In previous studies stochastic spectral moments were used to predict protein stability or function (González-Díaz, H. et al. Bioorg Med Chem 2005, 13, 323; Biopolymers 2005, 77, 296). Nevertheless, these moments take into consideration only electrostatic interactions and ignore other important factors such as van der Waals interactions. The present study introduces a new class of 3D structure molecular descriptors for folded proteins named the stochastic van der Waals spectral moments ((o)beta(k)). Among many possible applications, recognition of kinases was selected due to the fact that previous computational chemistry studies in this area have not been reported, despite the widespread distribution of kinases. The best linear model found was Kact = -9.44 degrees beta(0)(c) +10.94 degrees beta(5)(c) -2.40 degrees beta(0)(i) + 2.45 degrees beta(5)(m) + 0.73, where core (c), inner (i) and middle (m) refer to specific spatial protein regions. The model with a high Matthew's regression coefficient (0.79) correctly classified 206 out of 230 proteins (89.6%) including both training and predicting series. An area under the ROC curve of 0.94 differentiates our model from a random classifier. A subsequent principal components analysis of 152 heterogeneous proteins demonstrated that beta(k) codifies information different to other descriptors used in protein computational chemistry studies. Finally, the model recognizes 110 out of 125 kinases (88.0%) in a virtual screening experiment and this can be considered as an additional validation study (these proteins

  2. The role of skin colour in face recognition.

    PubMed

    Bar-Haim, Yair; Saidel, Talia; Yovel, Galit

    2009-01-01

    People have better memory for faces from their own racial group than for faces from other races. It has been suggested that this own-race recognition advantage depends on an initial categorisation of faces into own and other race based on racial markers, resulting in poorer encoding of individual variations in other-race faces. Here, we used a study--test recognition task with stimuli in which the skin colour of African and Caucasian faces was manipulated to produce four categories representing the cross-section between skin colour and facial features. We show that, despite the notion that skin colour plays a major role in categorising faces into own and other-race faces, its effect on face recognition is minor relative to differences across races in facial features.

  3. Unaware person recognition from the body when face identification fails.

    PubMed

    Rice, Allyson; Phillips, P Jonathon; Natu, Vaidehi; An, Xiaobo; O'Toole, Alice J

    2013-11-01

    How does one recognize a person when face identification fails? Here, we show that people rely on the body but are unaware of doing so. State-of-the-art face-recognition algorithms were used to select images of people with almost no useful identity information in the face. Recognition of the face alone in these cases was near chance level, but recognition of the person was accurate. Accuracy in identifying the person without the face was identical to that in identifying the whole person. Paradoxically, people reported relying heavily on facial features over noninternal face and body features in making their identity decisions. Eye movements indicated otherwise, with gaze duration and fixations shifting adaptively toward the body and away from the face when the body was a better indicator of identity than the face. This shift occurred with no cost to accuracy or response time. Human identity processing may be partially inaccessible to conscious awareness.

  4. Fast and accurate face recognition based on image compression

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng; Blasch, Erik

    2017-05-01

    Image compression is desired for many image-related applications especially for network-based applications with bandwidth and storage constraints. The face recognition community typical reports concentrate on the maximal compression rate that would not decrease the recognition accuracy. In general, the wavelet-based face recognition methods such as EBGM (elastic bunch graph matching) and FPB (face pattern byte) are of high performance but run slowly due to their high computation demands. The PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) algorithms run fast but perform poorly in face recognition. In this paper, we propose a novel face recognition method based on standard image compression algorithm, which is termed as compression-based (CPB) face recognition. First, all gallery images are compressed by the selected compression algorithm. Second, a mixed image is formed with the probe and gallery images and then compressed. Third, a composite compression ratio (CCR) is computed with three compression ratios calculated from: probe, gallery and mixed images. Finally, the CCR values are compared and the largest CCR corresponds to the matched face. The time cost of each face matching is about the time of compressing the mixed face image. We tested the proposed CPB method on the "ASUMSS face database" (visible and thermal images) from 105 subjects. The face recognition accuracy with visible images is 94.76% when using JPEG compression. On the same face dataset, the accuracy of FPB algorithm was reported as 91.43%. The JPEG-compressionbased (JPEG-CPB) face recognition is standard and fast, which may be integrated into a real-time imaging device.

  5. The asymmetric distribution of informative face information during gender recognition.

    PubMed

    Hu, Fengpei; Hu, Huan; Xu, Lian; Qin, Jungang

    2013-02-01

    Recognition of the gender of a face is important in social interactions. In the current study, the distribution of informative facial information was systematically examined during gender judgment using two methods, Bubbles and Focus windows techniques. Two experiments found that the most informative information was around the eyes, followed by the mouth and nose. Other parts of the face contributed to the gender recognition but were less important. The left side of the face was used more during gender recognition in two experiments. These results show mainly areas around the eyes are used for gender judgment and demonstrate perceptual asymmetry with a normal (non-chimeric) face.

  6. Face averages enhance user recognition for smartphone security.

    PubMed

    Robertson, David J; Kramer, Robin S S; Burton, A Mike

    2015-01-01

    Our recognition of familiar faces is excellent, and generalises across viewing conditions. However, unfamiliar face recognition is much poorer. For this reason, automatic face recognition systems might benefit from incorporating the advantages of familiarity. Here we put this to the test using the face verification system available on a popular smartphone (the Samsung Galaxy). In two experiments we tested the recognition performance of the smartphone when it was encoded with an individual's 'face-average'--a representation derived from theories of human face perception. This technique significantly improved performance for both unconstrained celebrity images (Experiment 1) and for real faces (Experiment 2): users could unlock their phones more reliably when the device stored an average of the user's face than when they stored a single image. This advantage was consistent across a wide variety of everyday viewing conditions. Furthermore, the benefit did not reduce the rejection of imposter faces. This benefit is brought about solely by consideration of suitable representations for automatic face recognition, and we argue that this is just as important as development of matching algorithms themselves. We propose that this representation could significantly improve recognition rates in everyday settings.

  7. Face Averages Enhance User Recognition for Smartphone Security

    PubMed Central

    Robertson, David J.; Kramer, Robin S. S.; Burton, A. Mike

    2015-01-01

    Our recognition of familiar faces is excellent, and generalises across viewing conditions. However, unfamiliar face recognition is much poorer. For this reason, automatic face recognition systems might benefit from incorporating the advantages of familiarity. Here we put this to the test using the face verification system available on a popular smartphone (the Samsung Galaxy). In two experiments we tested the recognition performance of the smartphone when it was encoded with an individual’s ‘face-average’ – a representation derived from theories of human face perception. This technique significantly improved performance for both unconstrained celebrity images (Experiment 1) and for real faces (Experiment 2): users could unlock their phones more reliably when the device stored an average of the user’s face than when they stored a single image. This advantage was consistent across a wide variety of everyday viewing conditions. Furthermore, the benefit did not reduce the rejection of imposter faces. This benefit is brought about solely by consideration of suitable representations for automatic face recognition, and we argue that this is just as important as development of matching algorithms themselves. We propose that this representation could significantly improve recognition rates in everyday settings. PMID:25807251

  8. Graph Laplace for occluded face completion and recognition.

    PubMed

    Deng, Yue; Dai, Qionghai; Zhang, Zengke

    2011-08-01

    This paper proposes a spectral-graph-based algorithm for face image repairing, which can improve the recognition performance on occluded faces. The face completion algorithm proposed in this paper includes three main procedures: 1) sparse representation for partially occluded face classification; 2) image-based data mining; and 3) graph Laplace (GL) for face image completion. The novel part of the proposed framework is GL, as named from graphical models and the Laplace equation, and can achieve a high-quality repairing of damaged or occluded faces. The relationship between the GL and the traditional Poisson equation is proven. We apply our face repairing algorithm to produce completed faces, and use face recognition to evaluate the performance of the algorithm. Experimental results verify the effectiveness of the GL method for occluded face completion.

  9. Real-time automated 3D sensing, detection, and recognition of dynamic biological micro-organic events

    NASA Astrophysics Data System (ADS)

    Javidi, Bahram; Yeom, Seokwon; Moon, Inkyu; Daneshpanah, Mehdi

    2006-05-01

    In this paper, we present an overview of three-dimensional (3D) optical imaging techniques for real-time automated sensing, visualization, and recognition of dynamic biological microorganisms. Real time sensing and 3D reconstruction of the dynamic biological microscopic objects can be performed by single-exposure on-line (SEOL) digital holographic microscopy. A coherent 3D microscope-based interferometer is constructed to record digital holograms of dynamic micro biological events. Complex amplitude 3D images of the biological microorganisms are computationally reconstructed at different depths by digital signal processing. Bayesian segmentation algorithms are applied to identify regions of interest for further processing. A number of pattern recognition approaches are addressed to identify and recognize the microorganisms. One uses 3D morphology of the microorganisms by analyzing 3D geometrical shapes which is composed of magnitude and phase. Segmentation, feature extraction, graph matching, feature selection, and training and decision rules are used to recognize the biological microorganisms. In a different approach, 3D technique is used that are tolerant to the varying shapes of the non-rigid biological microorganisms. After segmentation, a number of sampling patches are arbitrarily extracted from the complex amplitudes of the reconstructed 3D biological microorganism. These patches are processed using a number of cost functions and statistical inference theory for the equality of means and equality of variances between the sampling segments. Also, we discuss the possibility of employing computational integral imaging for 3D sensing, visualization, and recognition of biological microorganisms illuminated under incoherent light. Experimental results with several biological microorganisms are presented to illustrate detection, segmentation, and identification of micro biological events.

  10. The activation of visual face memory and explicit face recognition are delayed in developmental prosopagnosia.

    PubMed

    Parketny, Joanna; Towler, John; Eimer, Martin

    2015-08-01

    Individuals with developmental prosopagnosia (DP) are strongly impaired in recognizing faces, but the causes of this deficit are not well understood. We employed event-related brain potentials (ERPs) to study the time-course of neural processes involved in the recognition of previously unfamiliar faces in DPs and in age-matched control participants with normal face recognition abilities. Faces of different individuals were presented sequentially in one of three possible views, and participants had to detect a specific Target Face ("Joe"). EEG was recorded during task performance to Target Faces, Nontarget Faces, or the participants' Own Face (which had to be ignored). The N250 component was measured as a marker of the match between a seen face and a stored representation in visual face memory. The subsequent P600f was measured as an index of attentional processes associated with the conscious awareness and recognition of a particular face. Target Faces elicited reliable N250 and P600f in the DP group, but both of these components emerged later in DPs than in control participants. This shows that the activation of visual face memory for previously unknown learned faces and the subsequent attentional processing and conscious recognition of these faces are delayed in DP. N250 and P600f components to Own Faces did not differ between the two groups, indicating that the processing of long-term familiar faces is less affected in DP. However, P600f components to Own Faces were absent in two participants with DP who failed to recognize their Own Face during the experiment. These results provide new evidence that face recognition deficits in DP may be linked to a delayed activation of visual face memory and explicit identity recognition mechanisms.

  11. A new technique of recognition for coded targets in optical 3D measurement

    NASA Astrophysics Data System (ADS)

    Guo, Changye; Cheng, Xiaosheng; Cui, Haihua; Dai, Ning; Weng, Jinping

    2014-11-01

    A new technique for coded targets recognition in optical 3D-measurement application is proposed in this paper. Traditionally, point cloud registration is based on homologous features, such as the curvature, which is time-consuming and not reliable. For this, we paste some coded targets onto the surface of the object to be measured to improve the optimum target location and accurate correspondence among multi-source images. Circular coded targets are used, and an algorithm to automatically detecting them is proposed. This algorithm extracts targets with intensive bimodal histogram features from complex background, and filters noise according to their size, shape and intensity. In addition, the coded targets' identification is conducted out by their ring codes. We affine them around the circle inversely, set foreground and background respectively as 1 and 0 to constitute a binary number, and finally shift one bit every time to calculate a decimal one of the binary number to determine a minimum decimal number as its code. In this 3Dmeasurement application, we build a mutual relationship between different viewpoints containing three or more coded targets with different codes. Experiments show that it is of efficiency to obtain global surface data of an object to be measured and is robust to the projection angles and noise.

  12. Impaired processing of self-face recognition in anorexia nervosa.

    PubMed

    Hirot, France; Lesage, Marine; Pedron, Lya; Meyer, Isabelle; Thomas, Pierre; Cottencin, Olivier; Guardia, Dewi

    2016-03-01

    Body image disturbances and massive weight loss are major clinical symptoms of anorexia nervosa (AN). The aim of the present study was to examine the influence of body changes and eating attitudes on self-face recognition ability in AN. Twenty-seven subjects suffering from AN and 27 control participants performed a self-face recognition task (SFRT). During the task, digital morphs between their own face and a gender-matched unfamiliar face were presented in a random sequence. Participants' self-face recognition failures, cognitive flexibility, body concern and eating habits were assessed with the Self-Face Recognition Questionnaire (SFRQ), Trail Making Test (TMT), Body Shape Questionnaire (BSQ) and Eating Disorder Inventory-2 (EDI-2), respectively. Subjects suffering from AN exhibited significantly greater difficulties than control participants in identifying their own face (p = 0.028). No significant difference was observed between the two groups for TMT (all p > 0.1, non-significant). Regarding predictors of self-face recognition skills, there was a negative correlation between SFRT and body mass index (p = 0.01) and a positive correlation between SFRQ and EDI-2 (p < 0.001) or BSQ (p < 0.001). Among factors involved, nutritional status and intensity of eating disorders could play a part in impaired self-face recognition.

  13. A Neural Model of Face Recognition: a Comprehensive Approach

    NASA Astrophysics Data System (ADS)

    Stara, Vera; Montesanto, Anna; Puliti, Paolo; Tascini, Guido; Sechi, Cristina

    Visual recognition of faces is an essential behavior of humans: we have optimal performance in everyday life and just such a performance makes us able to establish the continuity of actors in our social life and to quickly identify and categorize people. This remarkable ability justifies the general interest in face recognition of researchers belonging to different fields and specially of designers of biometrical identification systems able to recognize the features of person's faces in a background. Due to interdisciplinary nature of this topic in this contribute we deal with face recognition through a comprehensive approach with the purpose to reproduce some features of human performance, as evidenced by studies in psychophysics and neuroscience, relevant to face recognition. This approach views face recognition as an emergent phenomenon resulting from the nonlinear interaction of a number of different features. For this reason our model of face recognition has been based on a computational system implemented through an artificial neural network. This synergy between neuroscience and engineering efforts allowed us to implement a model that had a biological plausibility, performed the same tasks as human subjects, and gave a possible account of human face perception and recognition. In this regard the paper reports on an experimental study of performance of a SOM-based neural network in a face recognition task, with reference both to the ability to learn to discriminate different faces, and to the ability to recognize a face already encountered in training phase, when presented in a pose or with an expression differing from the one present in the training context.

  14. The effect of distraction on face and voice recognition.

    PubMed

    Stevenage, Sarah V; Neil, Greg J; Barlow, Jess; Dyson, Amy; Eaton-Brown, Catherine; Parsons, Beth

    2013-03-01

    The results of two experiments are presented which explore the effect of distractor items on face and voice recognition. Following from the suggestion that voice processing is relatively weak compared to face processing, it was anticipated that voice recognition would be more affected by the presentation of distractor items between study and test compared to face recognition. Using a sequential matching task with a fixed interval between study and test that either incorporated distractor items or did not, the results supported our prediction. Face recognition remained strong irrespective of the number of distractor items between study and test. In contrast, voice recognition was significantly impaired by the presence of distractor items regardless of their number (Experiment 1). This pattern remained whether distractor items were highly similar to the targets or not (Experiment 2). These results offer support for the proposal that voice processing is a relatively vulnerable method of identification.

  15. Face Recognition Using Local Quantized Patterns and Gabor Filters

    NASA Astrophysics Data System (ADS)

    Khryashchev, V.; Priorov, A.; Stepanova, O.; Nikitin, A.

    2015-05-01

    The problem of face recognition in a natural or artificial environment has received a great deal of researchers' attention over the last few years. A lot of methods for accurate face recognition have been proposed. Nevertheless, these methods often fail to accurately recognize the person in difficult scenarios, e.g. low resolution, low contrast, pose variations, etc. We therefore propose an approach for accurate and robust face recognition by using local quantized patterns and Gabor filters. The estimation of the eye centers is used as a preprocessing stage. The evaluation of our algorithm on different samples from a standardized FERET database shows that our method is invariant to the general variations of lighting, expression, occlusion and aging. The proposed approach allows about 20% correct recognition accuracy increase compared with the known face recognition algorithms from the OpenCV library. The additional use of Gabor filters can significantly improve the robustness to changes in lighting conditions.

  16. Tolerance of geometric distortions in infant's face recognition.

    PubMed

    Yamashita, Wakayo; Kanazawa, So; Yamaguchi, Masami K

    2014-02-01

    The aim of the current study is to reveal the effect of global linear transformations (shearing, horizontal stretching, and vertical stretching) on the recognition of familiar faces (e.g., a mother's face) in 6- to 7-month-old infants. In this experiment, we applied the global linear transformations to both the infants' own mother's face and to a stranger's face, and we tested infants' preference between these faces. We found that only 7-month-old infants maintained preference for their own mother's face during the presentation of vertical stretching, while the preference for the mother's face disappeared during the presentation of shearing or horizontal stretching. These findings suggest that 7-month-old infants might not recognize faces based on calculating the absolute distance between facial features, and that the vertical dimension of facial features might be more related to infants' face recognition rather than the horizontal dimension. Copyright © 2013 Elsevier Inc. All rights reserved.

  17. Recognition of Faces of Ingroup and Outgroup Children and Adults

    ERIC Educational Resources Information Center

    Corenblum, B.; Meissner, Christian A.

    2006-01-01

    People are often more accurate in recognizing faces of ingroup members than in recognizing faces of outgroup members. Although own-group biases in face recognition are well established among adults, less attention has been given to such biases among children. This is surprising considering how often children give testimony in criminal and civil…

  18. Recognition of Faces of Ingroup and Outgroup Children and Adults

    ERIC Educational Resources Information Center

    Corenblum, B.; Meissner, Christian A.

    2006-01-01

    People are often more accurate in recognizing faces of ingroup members than in recognizing faces of outgroup members. Although own-group biases in face recognition are well established among adults, less attention has been given to such biases among children. This is surprising considering how often children give testimony in criminal and civil…

  19. Multi-feature fusion for thermal face recognition

    NASA Astrophysics Data System (ADS)

    Bi, Yin; Lv, Mingsong; Wei, Yangjie; Guan, Nan; Yi, Wang

    2016-07-01

    Human face recognition has been researched for the last three decades. Face recognition with thermal images now attracts significant attention since they can be used in low/none illuminated environment. However, thermal face recognition performance is still insufficient for practical applications. One main reason is that most existing work leverage only single feature to characterize a face in a thermal image. To solve the problem, we propose multi-feature fusion, a technique that combines multiple features in thermal face characterization and recognition. In this work, we designed a systematical way to combine four features, including Local binary pattern, Gabor jet descriptor, Weber local descriptor and Down-sampling feature. Experimental results show that our approach outperforms methods that leverage only a single feature and is robust to noise, occlusion, expression, low resolution and different l1 -minimization methods.

  20. Newborns' face recognition over changes in viewpoint.

    PubMed

    Turati, Chiara; Bulf, Hermann; Simion, Francesca

    2008-03-01

    The study investigated the origins of the ability to recognize faces despite rotations in depth. Four experiments are reported that tested, using the habituation technique, whether 1-to-3-day-old infants are able to recognize the invariant aspects of a face over changes in viewpoint. Newborns failed to recognize facial perceptual invariances between profile and full-face poses (Experiment 1), and profile and 3/4 poses (Experiment 3). Conversely, newborns recognized the identity of a face through full-face and 3/4 poses (Experiment 2). This result cannot be explained as a consequence of newborns' inability to discriminate between the full-face and 3/4 points of view (Experiment 4). Overall, evidence was provided that newborns are able to derive a representation of an unfamiliar face that is resilient to a certain degree of rotation in depth, from full-face to 3/4 and vice versa.

  1. Perception and recognition of faces in adolescence

    PubMed Central

    Fuhrmann, D.; Knoll, L. J.; Sakhardande, A. L.; Speekenbrink, M.; Kadosh, K. C.; Blakemore, S. -J.

    2016-01-01

    Most studies on the development of face cognition abilities have focussed on childhood, with early maturation accounts contending that face cognition abilities are mature by 3–5 years. Late maturation accounts, in contrast, propose that some aspects of face cognition are not mature until at least 10 years. Here, we measured face memory and face perception, two core face cognition abilities, in 661 participants (397 females) in four age groups (younger adolescents (11.27–13.38 years); mid-adolescents (13.39–15.89 years); older adolescents (15.90–18.00 years); and adults (18.01–33.15 years)) while controlling for differences in general cognitive ability. We showed that both face cognition abilities mature relatively late, at around 16 years, with a female advantage in face memory, but not in face perception, both in adolescence and adulthood. Late maturation in the face perception task was driven mainly by protracted development in identity perception, while gaze perception abilities were already comparatively mature in early adolescence. These improvements in the ability to memorize, recognize and perceive faces during adolescence may be related to increasing exploratory behaviour and exposure to novel faces during this period of life. PMID:27647477

  2. Integration of faces and voices, but not faces and names, in person recognition.

    PubMed

    O'Mahony, Christiane; Newell, Fiona N

    2012-02-01

    Recent studies on cross-modal recognition suggest that face and voice information are linked for the purpose of person identification. We tested whether congruent associations between familiarized faces and voices facilitated subsequent person recognition relative to incongruent associations. Furthermore, we investigated whether congruent face and name associations would similarly benefit person identification relative to incongruent face and name associations. Participants were familiarized with a set of talking video-images of actors, their names, and their voices. They were then tested on their recognition of either the face, voice, or name of each actor from bimodal stimuli which were either congruent or novel (incongruent) associations between the familiarized face and voice or face and name. We found that response times to familiarity decisions based on congruent face and voice stimuli were facilitated relative to incongruent associations. In contrast, we failed to find a benefit for congruent face and name pairs. Our findings suggest that faces and voices, but not faces and names, are integrated in memory for the purpose of person recognition. These findings have important implications for current models of face perception and support growing evidence for multisensory effects in face perception areas of the brain for the purpose of person recognition. ©2011 The British Psychological Society.

  3. Facial emotion recognition, face scan paths, and face perception in children with neurofibromatosis type 1.

    PubMed

    Lewis, Amelia K; Porter, Melanie A; Williams, Tracey A; Bzishvili, Samantha; North, Kathryn N; Payne, Jonathan M

    2017-05-01

    This study aimed to investigate face scan paths and face perception abilities in children with Neurofibromatosis Type 1 (NF1) and how these might relate to emotion recognition abilities in this population. The authors investigated facial emotion recognition, face scan paths, and face perception in 29 children with NF1 compared to 29 chronological age-matched typically developing controls. Correlations between facial emotion recognition, face scan paths, and face perception in children with NF1 were examined. Children with NF1 displayed significantly poorer recognition of fearful expressions compared to controls, as well as a nonsignificant trend toward poorer recognition of anger. Although there was no significant difference between groups in time spent viewing individual core facial features (eyes, nose, mouth, and nonfeature regions), children with NF1 spent significantly less time than controls viewing the face as a whole. Children with NF1 also displayed significantly poorer face perception abilities than typically developing controls. Facial emotion recognition deficits were not significantly associated with aberrant face scan paths or face perception abilities in the NF1 group. These results suggest that impairments in the perception, identification, and interpretation of information from faces are important aspects of the social-cognitive phenotype of NF1. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  4. Video-based face recognition via convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Bao, Tianlong; Ding, Chunhui; Karmoshi, Saleem; Zhu, Ming

    2017-06-01

    Face recognition has been widely studied recently while video-based face recognition still remains a challenging task because of the low quality and large intra-class variation of video captured face images. In this paper, we focus on two scenarios of video-based face recognition: 1)Still-to-Video(S2V) face recognition, i.e., querying a still face image against a gallery of video sequences; 2)Video-to-Still(V2S) face recognition, in contrast to S2V scenario. A novel method was proposed in this paper to transfer still and video face images to an Euclidean space by a carefully designed convolutional neural network, then Euclidean metrics are used to measure the distance between still and video images. Identities of still and video images that group as pairs are used as supervision. In the training stage, a joint loss function that measures the Euclidean distance between the predicted features of training pairs and expanding vectors of still images is optimized to minimize the intra-class variation while the inter-class variation is guaranteed due to the large margin of still images. Transferred features are finally learned via the designed convolutional neural network. Experiments are performed on COX face dataset. Experimental results show that our method achieves reliable performance compared with other state-of-the-art methods.

  5. The Impact of Early Bilingualism on Face Recognition Processes

    PubMed Central

    Kandel, Sonia; Burfin, Sabine; Méary, David; Ruiz-Tada, Elisa; Costa, Albert; Pascalis, Olivier

    2016-01-01

    Early linguistic experience has an impact on the way we decode audiovisual speech in face-to-face communication. The present study examined whether differences in visual speech decoding could be linked to a broader difference in face processing. To identify a phoneme we have to do an analysis of the speaker’s face to focus on the relevant cues for speech decoding (e.g., locating the mouth with respect to the eyes). Face recognition processes were investigated through two classic effects in face recognition studies: the Other-Race Effect (ORE) and the Inversion Effect. Bilingual and monolingual participants did a face recognition task with Caucasian faces (own race), Chinese faces (other race), and cars that were presented in an Upright or Inverted position. The results revealed that monolinguals exhibited the classic ORE. Bilinguals did not. Overall, bilinguals were slower than monolinguals. These results suggest that bilinguals’ face processing abilities differ from monolinguals’. Early exposure to more than one language may lead to a perceptual organization that goes beyond language processing and could extend to face analysis. We hypothesize that these differences could be due to the fact that bilinguals focus on different parts of the face than monolinguals, making them more efficient in other race face processing but slower. However, more studies using eye-tracking techniques are necessary to confirm this explanation. PMID:27486422

  6. The Impact of Early Bilingualism on Face Recognition Processes.

    PubMed

    Kandel, Sonia; Burfin, Sabine; Méary, David; Ruiz-Tada, Elisa; Costa, Albert; Pascalis, Olivier

    2016-01-01

    Early linguistic experience has an impact on the way we decode audiovisual speech in face-to-face communication. The present study examined whether differences in visual speech decoding could be linked to a broader difference in face processing. To identify a phoneme we have to do an analysis of the speaker's face to focus on the relevant cues for speech decoding (e.g., locating the mouth with respect to the eyes). Face recognition processes were investigated through two classic effects in face recognition studies: the Other-Race Effect (ORE) and the Inversion Effect. Bilingual and monolingual participants did a face recognition task with Caucasian faces (own race), Chinese faces (other race), and cars that were presented in an Upright or Inverted position. The results revealed that monolinguals exhibited the classic ORE. Bilinguals did not. Overall, bilinguals were slower than monolinguals. These results suggest that bilinguals' face processing abilities differ from monolinguals'. Early exposure to more than one language may lead to a perceptual organization that goes beyond language processing and could extend to face analysis. We hypothesize that these differences could be due to the fact that bilinguals focus on different parts of the face than monolinguals, making them more efficient in other race face processing but slower. However, more studies using eye-tracking techniques are necessary to confirm this explanation.

  7. 3D Exploration of Meteorological Data: Facing the challenges of operational forecasters

    NASA Astrophysics Data System (ADS)

    Koutek, Michal; Debie, Frans; van der Neut, Ian

    2016-04-01

    In the past years the Royal Netherlands Meteorological Institute (KNMI) has been working on innovation in the field of meteorological data visualization. We are dealing with Numerical Weather Prediction (NWP) model data and observational data, i.e. satellite images, precipitation radar, ground and air-borne measurements. These multidimensional multivariate data are geo-referenced and can be combined in 3D space to provide more intuitive views on the atmospheric phenomena. We developed the Weather3DeXplorer (W3DX), a visualization framework for processing and interactive exploration and visualization using Virtual Reality (VR) technology. We managed to have great successes with research studies on extreme weather situations. In this paper we will elaborate what we have learned from application of interactive 3D visualization in the operational weather room. We will explain how important it is to control the degrees-of-freedom during interaction that are given to the users: forecasters/scientists; (3D camera and 3D slicing-plane navigation appear to be rather difficult for the users, when not implemented properly). We will present a novel approach of operational 3D visualization user interfaces (UI) that for a great deal eliminates the obstacle and the time it usually takes to set up the visualization parameters and an appropriate camera view on a certain atmospheric phenomenon. We have found our inspiration in the way our operational forecasters work in the weather room. We decided to form a bridge between 2D visualization images and interactive 3D exploration. Our method combines WEB-based 2D UI's, pre-rendered 3D visualization catalog for the latest NWP model runs, with immediate entry into interactive 3D session for selected visualization setting. Finally, we would like to present the first user experiences with this approach.

  8. Original and Mirror Face Images and Minimum Squared Error Classification for Visible Light Face Recognition

    PubMed Central

    Wang, Rong

    2015-01-01

    In real-world applications, the image of faces varies with illumination, facial expression, and poses. It seems that more training samples are able to reveal possible images of the faces. Though minimum squared error classification (MSEC) is a widely used method, its applications on face recognition usually suffer from the problem of a limited number of training samples. In this paper, we improve MSEC by using the mirror faces as virtual training samples. We obtained the mirror faces generated from original training samples and put these two kinds of samples into a new set. The face recognition experiments show that our method does obtain high accuracy performance in classification. PMID:26576452

  9. Original and Mirror Face Images and Minimum Squared Error Classification for Visible Light Face Recognition.

    PubMed

    Wang, Rong

    2015-01-01

    In real-world applications, the image of faces varies with illumination, facial expression, and poses. It seems that more training samples are able to reveal possible images of the faces. Though minimum squared error classification (MSEC) is a widely used method, its applications on face recognition usually suffer from the problem of a limited number of training samples. In this paper, we improve MSEC by using the mirror faces as virtual training samples. We obtained the mirror faces generated from original training samples and put these two kinds of samples into a new set. The face recognition experiments show that our method does obtain high accuracy performance in classification.

  10. Multispectral face recognition using non linear dimensionality reduction

    NASA Astrophysics Data System (ADS)

    Akhloufi, Moulay A.; Bendada, Abdelhakim; Batsale, Jean-Christophe

    2009-05-01

    Face recognition in the infrared spectrum has attracted a lot of interest in recent years. Many of the techniques used in infrared are based on their visible counterpart, especially linear techniques like PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). In this work, we introduce non linear dimensionality reduction approaches for multispectral face recognition. For this purpose, the following techniques were developed: global non linear techniques (Kernel-PCA, Kernel-LDA) and local non linear techniques (Local Linear Embedding, Locality Preserving Projection). The performances of these techniques were compared to classical linear techniques for face recognition like PCA and LDA. Two multispectral face recognition databases were used in our experiments: Equinox Face Recognition Database and Laval University Database. Equinox database contains images in the Visible, Short, Mid and Long waves infrared spectrums. Laval database contains images in the Visible, Near, Mid and Long waves infrared spectrums with variations in time and metabolic activity of the subjects. The obtained results are interesting and show the increase in recognition performance using local non linear dimensionality reduction techniques for infrared face recognition, particularly in near and short wave infrared spectrums.

  11. Object Recognition in Flight: How Do Bees Distinguish between 3D Shapes?

    PubMed

    Werner, Annette; Stürzl, Wolfgang; Zanker, Johannes

    2016-01-01

    Honeybees (Apis mellifera) discriminate multiple object features such as colour, pattern and 2D shape, but it remains unknown whether and how bees recover three-dimensional shape. Here we show that bees can recognize objects by their three-dimensional form, whereby they employ an active strategy to uncover the depth profiles. We trained individual, free flying honeybees to collect sugar water from small three-dimensional objects made of styrofoam (sphere, cylinder, cuboids) or folded paper (convex, concave, planar) and found that bees can easily discriminate between these stimuli. We also tested possible strategies employed by the bees to uncover the depth profiles. For the card stimuli, we excluded overall shape and pictorial features (shading, texture gradients) as cues for discrimination. Lacking sufficient stereo vision, bees are known to use speed gradients in optic flow to detect edges; could the bees apply this strategy also to recover the fine details of a surface depth profile? Analysing the bees' flight tracks in front of the stimuli revealed specific combinations of flight maneuvers (lateral translations in combination with yaw rotations), which are particularly suitable to extract depth cues from motion parallax. We modelled the generated optic flow and found characteristic patterns of angular displacement corresponding to the depth profiles of our stimuli: optic flow patterns from pure translations successfully recovered depth relations from the magnitude of angular displacements, additional rotation provided robust depth information based on the direction of the displacements; thus, the bees flight maneuvers may reflect an optimized visuo-motor strategy to extract depth structure from motion signals. The robustness and simplicity of this strategy offers an efficient solution for 3D-object-recognition without stereo vision, and could be employed by other flying insects, or mobile robots.

  12. Object Recognition in Flight: How Do Bees Distinguish between 3D Shapes?

    PubMed Central

    Werner, Annette; Stürzl, Wolfgang; Zanker, Johannes

    2016-01-01

    Honeybees (Apis mellifera) discriminate multiple object features such as colour, pattern and 2D shape, but it remains unknown whether and how bees recover three-dimensional shape. Here we show that bees can recognize objects by their three-dimensional form, whereby they employ an active strategy to uncover the depth profiles. We trained individual, free flying honeybees to collect sugar water from small three-dimensional objects made of styrofoam (sphere, cylinder, cuboids) or folded paper (convex, concave, planar) and found that bees can easily discriminate between these stimuli. We also tested possible strategies employed by the bees to uncover the depth profiles. For the card stimuli, we excluded overall shape and pictorial features (shading, texture gradients) as cues for discrimination. Lacking sufficient stereo vision, bees are known to use speed gradients in optic flow to detect edges; could the bees apply this strategy also to recover the fine details of a surface depth profile? Analysing the bees’ flight tracks in front of the stimuli revealed specific combinations of flight maneuvers (lateral translations in combination with yaw rotations), which are particularly suitable to extract depth cues from motion parallax. We modelled the generated optic flow and found characteristic patterns of angular displacement corresponding to the depth profiles of our stimuli: optic flow patterns from pure translations successfully recovered depth relations from the magnitude of angular displacements, additional rotation provided robust depth information based on the direction of the displacements; thus, the bees flight maneuvers may reflect an optimized visuo-motor strategy to extract depth structure from motion signals. The robustness and simplicity of this strategy offers an efficient solution for 3D-object-recognition without stereo vision, and could be employed by other flying insects, or mobile robots. PMID:26886006

  13. Real-Time and High-Resolution 3D Face Measurement via a Smart Active Optical Sensor.

    PubMed

    You, Yong; Shen, Yang; Zhang, Guocai; Xing, Xiuwen

    2017-03-31

    The 3D measuring range and accuracy in traditional active optical sensing, such as Fourier transform profilometry, are influenced by the zero frequency of the captured patterns. The phase-shifting technique is commonly applied to remove the zero component. However, this phase-shifting method must capture several fringe patterns with phase difference, thereby influencing the real-time performance. This study introduces a smart active optical sensor, in which a composite pattern is utilized. The composite pattern efficiently combines several phase-shifting fringes and carrier frequencies. The method can remove zero frequency by using only one pattern. Model face reconstruction and human face measurement were employed to study the validity and feasibility of this method. Results show no distinct decrease in the precision of the novel method unlike the traditional phase-shifting method. The texture mapping technique was utilized to reconstruct a nature-appearance 3D digital face.

  14. Development of an Autonomous Face Recognition Machine.

    DTIC Science & Technology

    1986-12-08

    EXTERNAL FTNOU. INTEGER SUMFILE(27),IPIX(6144) REAL RINP (64),ARRAY(130),PARAM(16),CARRAY(64) INTEGER ISTAT(20),DUMMY(64),GESTDATA(300),PRNTARRAY(6,1O...260 CONTINUE C Do Gestalt on column CALL RTRAN(ARRAY,CARRAY, RINP ) BMAX- IR3D-O JR3D-O DO 280 1-1,64 IF( RINP (I).L. BMAX)GO TO 280 BDAX- RINP (T) IR3 D...ILLIN-1 DO 340 I.1,IENDU *IVAL-IFACTOR+I RIMP( I)-15-IPIX(IVAL) 340 CONrINUE DO 350 IsISENDW,64 RINP (I)-3.0 350 CONTINUE CALL RTRANSB(ARRAY, RINP ) NBAX

  15. Face engagement during infancy predicts later face recognition ability in younger siblings of children with autism.

    PubMed

    de Klerk, Carina C J M; Gliga, Teodora; Charman, Tony; Johnson, Mark H

    2014-07-01

    Face recognition difficulties are frequently documented in children with autism spectrum disorders (ASD). It has been hypothesized that these difficulties result from a reduced interest in faces early in life, leading to decreased cortical specialization and atypical development of the neural circuitry for face processing. However, a recent study by our lab demonstrated that infants at increased familial risk for ASD, irrespective of their diagnostic status at 3 years, exhibit a clear orienting response to faces. The present study was conducted as a follow-up on the same cohort to investigate how measures of early engagement with faces relate to face-processing abilities later in life. We also investigated whether face recognition difficulties are specifically related to an ASD diagnosis, or whether they are present at a higher rate in all those at familial risk. At 3 years we found a reduced ability to recognize unfamiliar faces in the high-risk group that was not specific to those children who received an ASD diagnosis, consistent with face recognition difficulties being an endophenotype of the disorder. Furthermore, we found that longer looking at faces at 7 months was associated with poorer performance on the face recognition task at 3 years in the high-risk group. These findings suggest that longer looking at faces in infants at risk for ASD might reflect early face-processing difficulties and predicts difficulties with recognizing faces later in life. © 2013 The Authors. Developmental Science Published by John Wiley & Sons Ltd.

  16. Face recognition in simulated prosthetic vision: face detection-based image processing strategies

    NASA Astrophysics Data System (ADS)

    Wang, Jing; Wu, Xiaobei; Lu, Yanyu; Wu, Hao; Kan, Han; Chai, Xinyu

    2014-08-01

    Objective. Given the limited visual percepts elicited by current prosthetic devices, it is essential to optimize image content in order to assist implant wearers to achieve better performance of visual tasks. This study focuses on recognition of familiar faces using simulated prosthetic vision. Approach. Combined with region-of-interest (ROI) magnification, three face extraction strategies based on a face detection technique were used: the Viola-Jones face region, the statistical face region (SFR) and the matting face region. Main results. These strategies significantly enhanced recognition performance compared to directly lowering resolution (DLR) with Gaussian dots. The inclusion of certain external features, such as hairstyle, was beneficial for face recognition. Given the high recognition accuracy achieved and applicable processing speed, SFR-ROI was the preferred strategy. DLR processing resulted in significant face gender recognition differences (i.e. females were more easily recognized than males), but these differences were not apparent with other strategies. Significance. Face detection-based image processing strategies improved visual perception by highlighting useful information. Their use is advisable for face recognition when using low-resolution prosthetic vision. These results provide information for the continued design of image processing modules for use in visual prosthetics, thus maximizing the benefits for future prosthesis wearers.

  17. Understanding eye movements in face recognition using hidden Markov models.

    PubMed

    Chuk, Tim; Chan, Antoni B; Hsiao, Janet H

    2014-09-16

    We use a hidden Markov model (HMM) based approach to analyze eye movement data in face recognition. HMMs are statistical models that are specialized in handling time-series data. We conducted a face recognition task with Asian participants, and model each participant's eye movement pattern with an HMM, which summarized the participant's scan paths in face recognition with both regions of interest and the transition probabilities among them. By clustering these HMMs, we showed that participants' eye movements could be categorized into holistic or analytic patterns, demonstrating significant individual differences even within the same culture. Participants with the analytic pattern had longer response times, but did not differ significantly in recognition accuracy from those with the holistic pattern. We also found that correct and wrong recognitions were associated with distinctive eye movement patterns; the difference between the two patterns lies in the transitions rather than locations of the fixations alone. © 2014 ARVO.

  18. Culture moderates the relationship between interdependence and face recognition.

    PubMed

    Ng, Andy H; Steele, Jennifer R; Sasaki, Joni Y; Sakamoto, Yumiko; Williams, Amanda

    2015-01-01

    Recent theory suggests that face recognition accuracy is affected by people's motivations, with people being particularly motivated to remember ingroup versus outgroup faces. In the current research we suggest that those higher in interdependence should have a greater motivation to remember ingroup faces, but this should depend on how ingroups are defined. To examine this possibility, we used a joint individual difference and cultural approach to test (a) whether individual differences in interdependence would predict face recognition accuracy, and (b) whether this effect would be moderated by culture. In Study 1 European Canadians higher in interdependence demonstrated greater recognition for same-race (White), but not cross-race (East Asian) faces. In Study 2 we found that culture moderated this effect. Interdependence again predicted greater recognition for same-race (White), but not cross-race (East Asian) faces among European Canadians; however, interdependence predicted worse recognition for both same-race (East Asian) and cross-race (White) faces among first-generation East Asians. The results provide insight into the role of motivation in face perception as well as cultural differences in the conception of ingroups.

  19. Culture moderates the relationship between interdependence and face recognition

    PubMed Central

    Ng, Andy H.; Steele, Jennifer R.; Sasaki, Joni Y.; Sakamoto, Yumiko; Williams, Amanda

    2015-01-01

    Recent theory suggests that face recognition accuracy is affected by people’s motivations, with people being particularly motivated to remember ingroup versus outgroup faces. In the current research we suggest that those higher in interdependence should have a greater motivation to remember ingroup faces, but this should depend on how ingroups are defined. To examine this possibility, we used a joint individual difference and cultural approach to test (a) whether individual differences in interdependence would predict face recognition accuracy, and (b) whether this effect would be moderated by culture. In Study 1 European Canadians higher in interdependence demonstrated greater recognition for same-race (White), but not cross-race (East Asian) faces. In Study 2 we found that culture moderated this effect. Interdependence again predicted greater recognition for same-race (White), but not cross-race (East Asian) faces among European Canadians; however, interdependence predicted worse recognition for both same-race (East Asian) and cross-race (White) faces among first-generation East Asians. The results provide insight into the role of motivation in face perception as well as cultural differences in the conception of ingroups. PMID:26579011

  20. Toward End-to-End Face Recognition Through Alignment Learning

    NASA Astrophysics Data System (ADS)

    Zhong, Yuanyi; Chen, Jiansheng; Huang, Bo

    2017-08-01

    Plenty of effective methods have been proposed for face recognition during the past decade. Although these methods differ essentially in many aspects, a common practice of them is to specifically align the facial area based on the prior knowledge of human face structure before feature extraction. In most systems, the face alignment module is implemented independently. This has actually caused difficulties in the designing and training of end-to-end face recognition models. In this paper we study the possibility of alignment learning in end-to-end face recognition, in which neither prior knowledge on facial landmarks nor artificially defined geometric transformations are required. Specifically, spatial transformer layers are inserted in front of the feature extraction layers in a Convolutional Neural Network (CNN) for face recognition. Only human identity clues are used for driving the neural network to automatically learn the most suitable geometric transformation and the most appropriate facial area for the recognition task. To ensure reproducibility, our model is trained purely on the publicly available CASIA-WebFace dataset, and is tested on the Labeled Face in the Wild (LFW) dataset. We have achieved a verification accuracy of 99.08\\% which is comparable to state-of-the-art single model based methods.

  1. Robust face recognition algorithm for identifition of disaster victims

    NASA Astrophysics Data System (ADS)

    Gevaert, Wouter J. R.; de With, Peter H. N.

    2013-02-01

    We present a robust face recognition algorithm for the identification of occluded, injured and mutilated faces with a limited training set per person. In such cases, the conventional face recognition methods fall short due to specific aspects in the classification. The proposed algorithm involves recursive Principle Component Analysis for reconstruction of afiected facial parts, followed by a feature extractor based on Gabor wavelets and uniform multi-scale Local Binary Patterns. As a classifier, a Radial Basis Neural Network is employed. In terms of robustness to facial abnormalities, tests show that the proposed algorithm outperforms conventional face recognition algorithms like, the Eigenfaces approach, Local Binary Patterns and the Gabor magnitude method. To mimic real-life conditions in which the algorithm would have to operate, specific databases have been constructed and merged with partial existing databases and jointly compiled. Experiments on these particular databases show that the proposed algorithm achieves recognition rates beyond 95%.

  2. Fusion of active and passive infrared images for face recognition

    NASA Astrophysics Data System (ADS)

    Akhloufi, Moulay A.; Bendada, Abdelhakim

    2013-05-01

    This work introduces a new framework for active and passive infrared image fusion for face recognition applications. Two multispectral face recognition databases were used in our experiments: Equinox Database (Visible, SWIR, MWIR, LWIR) and m-Faces Database (Visible, NIR, MWIR, LWIR). The proposed framework uses a fusion scheme in texture space in order to increase the performance of face recognition. The proposed texture space is based on the use of binary and ternary patterns. A new adaptive ternary pattern is also introduced. Active (SWIR and NIR) and passive (MWIR, LWIR) infrared modalities are used in this fusion scheme. An intraspectral and inter-spectral fusion approaches are introduced. The obtained results are promising and show an increase in the recognition performance when texture channels are fused in a multi-scale fusion scheme.

  3. 3D passive photon counting automatic target recognition using advanced correlation filters.

    PubMed

    Cho, Myungjin; Mahalanobis, Abhijit; Javidi, Bahram

    2011-03-15

    In this Letter, we present results for detecting and recognizing 3D objects in photon counting images using integral imaging with maximum average correlation height filters. We show that even under photon starved conditions objects may be automatically recognized in passively sensed 3D images using advanced correlation filters. We show that the proposed filter synthesized with ideal training images can detect and recognize a 3D object in photon counting images, even in the presence of occlusions and obscuration.

  4. Do people have insight into their face recognition abilities?

    PubMed

    Palermo, Romina; Rossion, Bruno; Rhodes, Gillian; Laguesse, Renaud; Tez, Tolga; Hall, Bronwyn; Albonico, Andrea; Malaspina, Manuela; Daini, Roberta; Irons, Jessica; Al-Janabi, Shahd; Taylor, Libby C; Rivolta, Davide; McKone, Elinor

    2017-02-01

    Diagnosis of developmental or congenital prosopagnosia (CP) involves self-report of everyday face recognition difficulties, which are corroborated with poor performance on behavioural tests. This approach requires accurate self-evaluation. We examine the extent to which typical adults have insight into their face recognition abilities across four experiments involving nearly 300 participants. The experiments used five tests of face recognition ability: two that tap into the ability to learn and recognize previously unfamiliar faces [the Cambridge Face Memory Test, CFMT; Duchaine, B., & Nakayama, K. (2006). The Cambridge Face Memory Test: Results for neurologically intact individuals and an investigation of its validity using inverted face stimuli and prosopagnosic participants. Neuropsychologia, 44(4), 576-585. doi:10.1016/j.neuropsychologia.2005.07.001; and a newly devised test based on the CFMT but where the study phases involve watching short movies rather than viewing static faces-the CFMT-Films] and three that tap face matching [Benton Facial Recognition Test, BFRT; Benton, A., Sivan, A., Hamsher, K., Varney, N., & Spreen, O. (1983). Contribution to neuropsychological assessment. New York: Oxford University Press; and two recently devised sequential face matching tests]. Self-reported ability was measured with the 15-item Kennerknecht et al. questionnaire [Kennerknecht, I., Ho, N. Y., & Wong, V. C. (2008). Prevalence of hereditary prosopagnosia (HPA) in Hong Kong Chinese population. American Journal of Medical Genetics Part A, 146A(22), 2863-2870. doi:10.1002/ajmg.a.32552]; two single-item questions assessing face recognition ability; and a new 77-item meta-cognition questionnaire. Overall, we find that adults with typical face recognition abilities have only modest insight into their ability to recognize faces on behavioural tests. In a fifth experiment, we assess self-reported face recognition ability in people with CP and find that some people who expect to

  5. Face recognition performance of individuals with Asperger syndrome on the Cambridge Face Memory Test.

    PubMed

    Hedley, Darren; Brewer, Neil; Young, Robyn

    2011-12-01

    Although face recognition deficits in individuals with Autism Spectrum Disorder (ASD), including Asperger syndrome (AS), are widely acknowledged, the empirical evidence is mixed. This in part reflects the failure to use standardized and psychometrically sound tests. We contrasted standardized face recognition scores on the Cambridge Face Memory Test (CFMT) for 34 individuals with AS with those for 42, IQ-matched non-ASD individuals, and age-standardized scores from a large Australian cohort. We also examined the influence of IQ, autistic traits, and negative affect on face recognition performance. Overall, participants with AS performed significantly worse on the CFMT than the non-ASD participants and when evaluated against standardized test norms. However, while 24% of participants with AS presented with severe face recognition impairment (>2 SDs below the mean), many individuals performed at or above the typical level for their age: 53% scored within +/- 1 SD of the mean and 9% demonstrated superior performance (>1 SD above the mean). Regression analysis provided no evidence that IQ, autistic traits, or negative affect significantly influenced face recognition: diagnostic group membership was the only significant predictor of face recognition performance. In sum, face recognition performance in ASD is on a continuum, but with average levels significantly below non-ASD levels of performance.

  6. What Types of Visual Recognition Tasks Are Mediated by the Neural Subsystem that Subserves Face Recognition?

    ERIC Educational Resources Information Center

    Brooks, Brian E.; Cooper, Eric E.

    2006-01-01

    Three divided visual field experiments tested current hypotheses about the types of visual shape representation tasks that recruit the cognitive and neural mechanisms underlying face recognition. Experiment 1 found a right hemisphere advantage for subordinate but not basic-level face recognition. Experiment 2 found a right hemisphere advantage for…

  7. What Types of Visual Recognition Tasks Are Mediated by the Neural Subsystem that Subserves Face Recognition?

    ERIC Educational Resources Information Center

    Brooks, Brian E.; Cooper, Eric E.

    2006-01-01

    Three divided visual field experiments tested current hypotheses about the types of visual shape representation tasks that recruit the cognitive and neural mechanisms underlying face recognition. Experiment 1 found a right hemisphere advantage for subordinate but not basic-level face recognition. Experiment 2 found a right hemisphere advantage for…

  8. Image dependency in the recognition of newly learnt faces.

    PubMed

    Longmore, Christopher A; Santos, Isabel M; Silva, Carlos F; Hall, Abi; Faloyin, Dipo; Little, Emily

    2017-05-01

    Research investigating the effect of lighting and viewpoint changes on unfamiliar and newly learnt faces has revealed that such recognition is highly image dependent and that changes in either of these leads to poor recognition accuracy. Three experiments are reported to extend these findings by examining the effect of apparent age on the recognition of newly learnt faces. Experiment 1 investigated the ability to generalize to novel ages of a face after learning a single image. It was found that recognition was best for the learnt image with performance falling the greater the dissimilarity between the study and test images. Experiments 2 and 3 examined whether learning two images aids subsequent recognition of a novel image. The results indicated that interpolation between two studied images (Experiment 2) provided some additional benefit over learning a single view, but that this did not extend to extrapolation (Experiment 3). The results from all studies suggest that recognition was driven primarily by pictorial codes and that the recognition of faces learnt from a limited number of sources operates on stored images of faces as opposed to more abstract, structural, representations.

  9. Interaction of face and voice areas during speaker recognition.

    PubMed

    von Kriegstein, Katharina; Kleinschmidt, Andreas; Sterzer, Philipp; Giraud, Anne-Lise

    2005-03-01

    Face and voice processing contribute to person recognition, but it remains unclear how the segregated specialized cortical modules interact. Using functional neuroimaging, we observed cross-modal responses to voices of familiar persons in the fusiform face area, as localized separately using visual stimuli. Voices of familiar persons only activated the face area during a task that emphasized speaker recognition over recognition of verbal content. Analyses of functional connectivity between cortical territories show that the fusiform face region is coupled with the superior temporal sulcus voice region during familiar speaker recognition, but not with any of the other cortical regions normally active in person recognition or in other tasks involving voices. These findings are relevant for models of the cognitive processes and neural circuitry involved in speaker recognition. They reveal that in the context of speaker recognition, the assessment of person familiarity does not necessarily engage supramodal cortical substrates but can result from the direct sharing of information between auditory voice and visual face regions.

  10. Newborns' Face Recognition over Changes in Viewpoint

    ERIC Educational Resources Information Center

    Turati, Chiara; Bulf, Hermann; Simion, Francesca

    2008-01-01

    The study investigated the origins of the ability to recognize faces despite rotations in depth. Four experiments are reported that tested, using the habituation technique, whether 1-to-3-day-old infants are able to recognize the invariant aspects of a face over changes in viewpoint. Newborns failed to recognize facial perceptual invariances…

  11. Newborns' Face Recognition over Changes in Viewpoint

    ERIC Educational Resources Information Center

    Turati, Chiara; Bulf, Hermann; Simion, Francesca

    2008-01-01

    The study investigated the origins of the ability to recognize faces despite rotations in depth. Four experiments are reported that tested, using the habituation technique, whether 1-to-3-day-old infants are able to recognize the invariant aspects of a face over changes in viewpoint. Newborns failed to recognize facial perceptual invariances…

  12. Distinctive voices enhance the visual recognition of unfamiliar faces.

    PubMed

    Bülthoff, I; Newell, F N

    2015-04-01

    Several studies have provided evidence in favour of a norm-based representation of faces in memory. However, such models have hitherto failed to take account of how other person-relevant information affects face recognition performance. Here we investigated whether distinctive or typical auditory stimuli affect the subsequent recognition of previously unfamiliar faces and whether the type of auditory stimulus matters. In this study participants learned to associate either unfamiliar distinctive and typical voices or unfamiliar distinctive and typical sounds to unfamiliar faces. The results indicated that recognition performance was better to faces previously paired with distinctive than with typical voices but we failed to find any benefit on face recognition when the faces were previously associated with distinctive sounds. These findings possibly point to an expertise effect, as faces are usually associated to voices. More importantly, it suggests that the memory for visual faces can be modified by the perceptual quality of related vocal information and more specifically that facial distinctiveness can be of a multi-sensory nature. These results have important implications for our understanding of the structure of memory for person identification. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

  13. Improving cross-modal face recognition using polarimetric imaging.

    PubMed

    Short, Nathaniel; Hu, Shuowen; Gurram, Prudhvi; Gurton, Kristan; Chan, Alex

    2015-03-15

    We investigate the performance of polarimetric imaging in the long-wave infrared (LWIR) spectrum for cross-modal face recognition. For this work, polarimetric imagery is generated as stacks of three components: the conventional thermal intensity image (referred to as S0), and the two Stokes images, S1 and S2, which contain combinations of different polarizations. The proposed face recognition algorithm extracts and combines local gradient magnitude and orientation information from S0, S1, and S2 to generate a robust feature set that is well-suited for cross-modal face recognition. Initial results show that polarimetric LWIR-to-visible face recognition achieves an 18% increase in Rank-1 identification rate compared to conventional LWIR-to-visible face recognition. We conclude that a substantial improvement in automatic face recognition performance can be achieved by exploiting the polarization-state of radiance, as compared to using conventional thermal imagery.

  14. Lateralization of kin recognition signals in the human face

    PubMed Central

    Dal Martello, Maria F.; Maloney, Laurence T.

    2010-01-01

    When human subjects view photographs of faces, their judgments of identity, gender, emotion, age, and attractiveness depend more on one side of the face than the other. We report an experiment testing whether allocentric kin recognition (the ability to judge the degree of kinship between individuals other than the observer) is also lateralized. One hundred and twenty-four observers judged whether or not pairs of children were biological siblings by looking at photographs of their faces. In three separate conditions, (1) the right hemi-face was masked, (2) the left hemi-face was masked, or (3) the face was fully visible. The d′ measures for the masked left hemi-face and masked right hemi-face were 1.024 and 1.004, respectively (no significant difference), and the d′ measure for the unmasked face was 1.079, not significantly greater than that for either of the masked conditions. We conclude, first, that there is no superiority of one or the other side of the observed face in kin recognition, second, that the information present in the left and right hemi-faces relevant to recognizing kin is completely redundant, and last that symmetry cues are not used for kin recognition. PMID:20884584

  15. Intact recognition of facial expression, gender, and age in patients with impaired recognition of face identity.

    PubMed

    Tranel, D; Damasio, A R; Damasio, H

    1988-05-01

    We conducted a series of experiments to assess the ability to recognize the meaning of facial expressions, gender, and age in four patients with severe impairments of the recognition of facial identity. In three patients the recognition of face identity could be dissociated from that of facial expression, age, and gender. In one, all forms of face recognition were impaired. Thus, a given lesion may preclude one type of recognition but not another. We conclude that (1) the cognitive demands posed by different forms of recognition are met at different processing levels, and (2) different levels depend on different neural substrates.

  16. CT Image Sequence Analysis for Object Recognition - A Rule-Based 3-D Computer Vision System

    Treesearch

    Dongping Zhu; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman

    1991-01-01

    Research is now underway to create a vision system for hardwood log inspection using a knowledge-based approach. In this paper, we present a rule-based, 3-D vision system for locating and identifying wood defects using topological, geometric, and statistical attributes. A number of different features can be derived from the 3-D input scenes. These features and evidence...

  17. Error Rates in Users of Automatic Face Recognition Software.

    PubMed

    White, David; Dunn, James D; Schmid, Alexandra C; Kemp, Richard I

    2015-01-01

    In recent years, wide deployment of automatic face recognition systems has been accompanied by substantial gains in algorithm performance. However, benchmarking tests designed to evaluate these systems do not account for the errors of human operators, who are often an integral part of face recognition solutions in forensic and security settings. This causes a mismatch between evaluation tests and operational accuracy. We address this by measuring user performance in a face recognition system used to screen passport applications for identity fraud. Experiment 1 measured target detection accuracy in algorithm-generated 'candidate lists' selected from a large database of passport images. Accuracy was notably poorer than in previous studies of unfamiliar face matching: participants made over 50% errors for adult target faces, and over 60% when matching images of children. Experiment 2 then compared performance of student participants to trained passport officers-who use the system in their daily work-and found equivalent performance in these groups. Encouragingly, a group of highly trained and experienced "facial examiners" outperformed these groups by 20 percentage points. We conclude that human performance curtails accuracy of face recognition systems-potentially reducing benchmark estimates by 50% in operational settings. Mere practise does not attenuate these limits, but superior performance of trained examiners suggests that recruitment and selection of human operators, in combination with effective training and mentorship, can improve the operational accuracy of face recognition systems.

  18. Error Rates in Users of Automatic Face Recognition Software

    PubMed Central

    White, David; Dunn, James D.; Schmid, Alexandra C.; Kemp, Richard I.

    2015-01-01

    In recent years, wide deployment of automatic face recognition systems has been accompanied by substantial gains in algorithm performance. However, benchmarking tests designed to evaluate these systems do not account for the errors of human operators, who are often an integral part of face recognition solutions in forensic and security settings. This causes a mismatch between evaluation tests and operational accuracy. We address this by measuring user performance in a face recognition system used to screen passport applications for identity fraud. Experiment 1 measured target detection accuracy in algorithm-generated ‘candidate lists’ selected from a large database of passport images. Accuracy was notably poorer than in previous studies of unfamiliar face matching: participants made over 50% errors for adult target faces, and over 60% when matching images of children. Experiment 2 then compared performance of student participants to trained passport officers–who use the system in their daily work–and found equivalent performance in these groups. Encouragingly, a group of highly trained and experienced “facial examiners” outperformed these groups by 20 percentage points. We conclude that human performance curtails accuracy of face recognition systems–potentially reducing benchmark estimates by 50% in operational settings. Mere practise does not attenuate these limits, but superior performance of trained examiners suggests that recruitment and selection of human operators, in combination with effective training and mentorship, can improve the operational accuracy of face recognition systems. PMID:26465631

  19. Face Engagement during Infancy Predicts Later Face Recognition Ability in Younger Siblings of Children with Autism

    ERIC Educational Resources Information Center

    de Klerk, Carina C. J. M.; Gliga, Teodora; Charman, Tony; Johnson, Mark H.

    2014-01-01

    Face recognition difficulties are frequently documented in children with autism spectrum disorders (ASD). It has been hypothesized that these difficulties result from a reduced interest in faces early in life, leading to decreased cortical specialization and atypical development of the neural circuitry for face processing. However, a recent study…

  20. Developmental Changes in Face Recognition during Childhood: Evidence from Upright and Inverted Faces

    ERIC Educational Resources Information Center

    de Heering, Adelaide; Rossion, Bruno; Maurer, Daphne

    2012-01-01

    Adults are experts at recognizing faces but there is controversy about how this ability develops with age. We assessed 6- to 12-year-olds and adults using a digitized version of the Benton Face Recognition Test, a sensitive tool for assessing face perception abilities. Children's response times for correct responses did not decrease between ages 6…

  1. Face Engagement during Infancy Predicts Later Face Recognition Ability in Younger Siblings of Children with Autism

    ERIC Educational Resources Information Center

    de Klerk, Carina C. J. M.; Gliga, Teodora; Charman, Tony; Johnson, Mark H.

    2014-01-01

    Face recognition difficulties are frequently documented in children with autism spectrum disorders (ASD). It has been hypothesized that these difficulties result from a reduced interest in faces early in life, leading to decreased cortical specialization and atypical development of the neural circuitry for face processing. However, a recent study…

  2. Developmental Changes in Face Recognition during Childhood: Evidence from Upright and Inverted Faces

    ERIC Educational Resources Information Center

    de Heering, Adelaide; Rossion, Bruno; Maurer, Daphne

    2012-01-01

    Adults are experts at recognizing faces but there is controversy about how this ability develops with age. We assessed 6- to 12-year-olds and adults using a digitized version of the Benton Face Recognition Test, a sensitive tool for assessing face perception abilities. Children's response times for correct responses did not decrease between ages 6…

  3. Robust face recognition using fusion of multiscale experts

    NASA Astrophysics Data System (ADS)

    Naseem, Imran; Alim, M. Affan

    2012-04-01

    This paper proposes an efficient and robust technique for face recognition. The proposed technique includes the Daubechie's wavelet transform D10, Principal Component Analysis (PCA) and Multiscale fusion for face recognition. Features are extracted using the PCA on original and multiscale images. The multiscale fusion is used to combine the results of PCA and wavelet transformed PCA to achieve better performance. The main idea is to utilize the discriminant information of various subbands rather than relying on a single scale. Multiscale experts are finally fused using the sum rule. Extensive experimental results on the AT&T database show that recognition performance is improved by the proposed method.

  4. Neural correlates of recognition memory for emotional faces and scenes

    PubMed Central

    Chiew, Kimberly S.; Anderson, John A. E.; Grady, Cheryl L.

    2011-01-01

    We examined the influence of emotional valence and type of item to be remembered on brain activity during recognition, using faces and scenes. We used multivariate analyses of event-related fMRI data to identify whole-brain patterns, or networks of activity. Participants demonstrated better recognition for scenes vs faces and for negative vs neutral and positive items. Activity was increased in extrastriate cortex and inferior frontal gyri for emotional scenes, relative to neutral scenes and all face types. Increased activity in these regions also was seen for negative faces relative to positive faces. Correct recognition of negative faces and scenes (hits vs correct rejections) was associated with increased activity in amygdala, hippocampus, extrastriate, frontal and parietal cortices. Activity specific to correctly recognized emotional faces, but not scenes, was found in sensorimotor areas and rostral prefrontal cortex. These results suggest that emotional valence and type of visual stimulus both modulate brain activity at recognition, and influence multiple networks mediating visual, memory and emotion processing. The contextual information in emotional scenes may facilitate memory via additional visual processing, whereas memory for emotional faces may rely more on cognitive control mediated by rostrolateral prefrontal regions. PMID:20194514

  5. Neural correlates of recognition memory for emotional faces and scenes.

    PubMed

    Keightley, Michelle L; Chiew, Kimberly S; Anderson, John A E; Grady, Cheryl L

    2011-01-01

    We examined the influence of emotional valence and type of item to be remembered on brain activity during recognition, using faces and scenes. We used multivariate analyses of event-related fMRI data to identify whole-brain patterns, or networks of activity. Participants demonstrated better recognition for scenes vs faces and for negative vs neutral and positive items. Activity was increased in extrastriate cortex and inferior frontal gyri for emotional scenes, relative to neutral scenes and all face types. Increased activity in these regions also was seen for negative faces relative to positive faces. Correct recognition of negative faces and scenes (hits vs correct rejections) was associated with increased activity in amygdala, hippocampus, extrastriate, frontal and parietal cortices. Activity specific to correctly recognized emotional faces, but not scenes, was found in sensorimotor areas and rostral prefrontal cortex. These results suggest that emotional valence and type of visual stimulus both modulate brain activity at recognition, and influence multiple networks mediating visual, memory and emotion processing. The contextual information in emotional scenes may facilitate memory via additional visual processing, whereas memory for emotional faces may rely more on cognitive control mediated by rostrolateral prefrontal regions.

  6. Supervised Filter Learning for Representation Based Face Recognition

    PubMed Central

    Bi, Chao; Zhang, Lei; Qi, Miao; Zheng, Caixia; Yi, Yugen; Wang, Jianzhong; Zhang, Baoxue

    2016-01-01

    Representation based classification methods, such as Sparse Representation Classification (SRC) and Linear Regression Classification (LRC) have been developed for face recognition problem successfully. However, most of these methods use the original face images without any preprocessing for recognition. Thus, their performances may be affected by some problematic factors (such as illumination and expression variances) in the face images. In order to overcome this limitation, a novel supervised filter learning algorithm is proposed for representation based face recognition in this paper. The underlying idea of our algorithm is to learn a filter so that the within-class representation residuals of the faces' Local Binary Pattern (LBP) features are minimized and the between-class representation residuals of the faces' LBP features are maximized. Therefore, the LBP features of filtered face images are more discriminative for representation based classifiers. Furthermore, we also extend our algorithm for heterogeneous face recognition problem. Extensive experiments are carried out on five databases and the experimental results verify the efficacy of the proposed algorithm. PMID:27416030

  7. The Development of Spatial Frequency Biases in Face Recognition

    ERIC Educational Resources Information Center

    Leonard, Hayley C.; Karmiloff-Smith, Annette; Johnson, Mark H.

    2010-01-01

    Previous research has suggested that a mid-band of spatial frequencies is critical to face recognition in adults, but few studies have explored the development of this bias in children. We present a paradigm adapted from the adult literature to test spatial frequency biases throughout development. Faces were presented on a screen with particular…

  8. The Development of Spatial Frequency Biases in Face Recognition

    ERIC Educational Resources Information Center

    Leonard, Hayley C.; Karmiloff-Smith, Annette; Johnson, Mark H.

    2010-01-01

    Previous research has suggested that a mid-band of spatial frequencies is critical to face recognition in adults, but few studies have explored the development of this bias in children. We present a paradigm adapted from the adult literature to test spatial frequency biases throughout development. Faces were presented on a screen with particular…

  9. Development of Face Recognition in Infant Chimpanzees (Pan Troglodytes)

    ERIC Educational Resources Information Center

    Myowa-Yamakoshi, M.; Yamaguchi, M.K.; Tomonaga, M.; Tanaka, M.; Matsuzawa, T.

    2005-01-01

    In this paper, we assessed the developmental changes in face recognition by three infant chimpanzees aged 1-18 weeks, using preferential-looking procedures that measured the infants' eye- and head-tracking of moving stimuli. In Experiment 1, we prepared photographs of the mother of each infant and an ''average'' chimpanzee face using…

  10. Development of Face Recognition in Infant Chimpanzees (Pan Troglodytes)

    ERIC Educational Resources Information Center

    Myowa-Yamakoshi, M.; Yamaguchi, M.K.; Tomonaga, M.; Tanaka, M.; Matsuzawa, T.

    2005-01-01

    In this paper, we assessed the developmental changes in face recognition by three infant chimpanzees aged 1-18 weeks, using preferential-looking procedures that measured the infants' eye- and head-tracking of moving stimuli. In Experiment 1, we prepared photographs of the mother of each infant and an ''average'' chimpanzee face using…

  11. Recognition Profile of Emotions in Natural and Virtual Faces

    PubMed Central

    Dyck, Miriam; Winbeck, Maren; Leiberg, Susanne; Chen, Yuhan; Gur, Rurben C.; Mathiak, Klaus

    2008-01-01

    Background Computer-generated virtual faces become increasingly realistic including the simulation of emotional expressions. These faces can be used as well-controlled, realistic and dynamic stimuli in emotion research. However, the validity of virtual facial expressions in comparison to natural emotion displays still needs to be shown for the different emotions and different age groups. Methodology/Principal Findings Thirty-two healthy volunteers between the age of 20 and 60 rated pictures of natural human faces and faces of virtual characters (avatars) with respect to the expressed emotions: happiness, sadness, anger, fear, disgust, and neutral. Results indicate that virtual emotions were recognized comparable to natural ones. Recognition differences in virtual and natural faces depended on specific emotions: whereas disgust was difficult to convey with the current avatar technology, virtual sadness and fear achieved better recognition results than natural faces. Furthermore, emotion recognition rates decreased for virtual but not natural faces in participants over the age of 40. This specific age effect suggests that media exposure has an influence on emotion recognition. Conclusions/Significance Virtual and natural facial displays of emotion may be equally effective. Improved technology (e.g. better modelling of the naso-labial area) may lead to even better results as compared to trained actors. Due to the ease with which virtual human faces can be animated and manipulated, validated artificial emotional expressions will be of major relevance in future research and therapeutic applications. PMID:18985152

  12. Supervised Filter Learning for Representation Based Face Recognition.

    PubMed

    Bi, Chao; Zhang, Lei; Qi, Miao; Zheng, Caixia; Yi, Yugen; Wang, Jianzhong; Zhang, Baoxue

    2016-01-01

    Representation based classification methods, such as Sparse Representation Classification (SRC) and Linear Regression Classification (LRC) have been developed for face recognition problem successfully. However, most of these methods use the original face images without any preprocessing for recognition. Thus, their performances may be affected by some problematic factors (such as illumination and expression variances) in the face images. In order to overcome this limitation, a novel supervised filter learning algorithm is proposed for representation based face recognition in this paper. The underlying idea of our algorithm is to learn a filter so that the within-class representation residuals of the faces' Local Binary Pattern (LBP) features are minimized and the between-class representation residuals of the faces' LBP features are maximized. Therefore, the LBP features of filtered face images are more discriminative for representation based classifiers. Furthermore, we also extend our algorithm for heterogeneous face recognition problem. Extensive experiments are carried out on five databases and the experimental results verify the efficacy of the proposed algorithm.

  13. A method for 3D scene recognition using shadow information and a single fixed viewpoint

    NASA Astrophysics Data System (ADS)

    Bamber, David C.; Rogers, Jeremy D.; Page, Scott F.

    2012-05-01

    The ability to passively reconstruct a scene in 3D provides significant benefit to Situational Awareness systems employed in security and surveillance applications. Traditionally, passive 3D scene modelling techniques, such as Shape from Silhouette, require images from multiple sensor viewpoints, acquired either through the motion of a single sensor or from multiple sensors. As a result, the application of these techniques often attracts high costs, and presents numerous practical challenges. This paper presents a 3D scene reconstruction approach based on exploiting scene shadows, which only requires information from a single static sensor. This paper demonstrates that a large amount of 3D information about a scene can be interpreted from shadows; shadows reveal the shape of objects as viewed from a solar perspective and additional perspectives are gained as the sun arcs across the sky. The approach has been tested on synthetic and real data and is shown to be capable of reconstructing 3D scene objects where traditional 3D imaging methods fail. Providing the shadows within a scene are discernible, the proposed technique is able to reconstruct 3D objects that are camouflaged, obscured or even outside of the sensor's Field of View. The proposed approach can be applied in a range of applications, for example urban surveillance, checkpoint and border control, critical infrastructure protection and for identifying concealed or suspicious objects or persons which would normally be hidden from the sensor viewpoint.

  14. Is principal component analysis an effective tool to predict face attractiveness? A contribution based on real 3D faces of highly selected attractive women, scanned with stereophotogrammetry.

    PubMed

    Galantucci, Luigi Maria; Di Gioia, Eliana; Lavecchia, Fulvio; Percoco, Gianluca

    2014-05-01

    In the literature, several papers report studies on mathematical models used to describe facial features and to predict female facial beauty based on 3D human face data. Many authors have proposed the principal component analysis (PCA) method that permits modeling of the entire human face using a limited number of parameters. In some cases, these models have been correlated with beauty classifications, obtaining good attractiveness predictability using wrapped 2D or 3D models. To verify these results, in this paper, the authors conducted a three-dimensional digitization study of 66 very attractive female subjects using a computerized noninvasive tool known as 3D digital photogrammetry. The sample consisted of the 64 contestants of the final phase of the Miss Italy 2010 beauty contest, plus the two highest ranked contestants in the 2009 competition. PCA was conducted on this real faces sample to verify if there is a correlation between ranking and the principal components of the face models. There was no correlation and therefore, this hypothesis is not confirmed for our sample. Considering that the results of the contest are not only solely a function of facial attractiveness, but undoubtedly are significantly impacted by it, the authors based on their experience and real faces conclude that PCA analysis is not a valid prediction tool for attractiveness. The database of the features belonging to the sample analyzed are downloadable online and further contributions are welcome.

  15. The own-age face recognition bias is task dependent.

    PubMed

    Proietti, Valentina; Macchi Cassia, Viola; Mondloch, Catherine J

    2015-08-01

    The own-age bias (OAB) in face recognition (more accurate recognition of own-age than other-age faces) is robust among young adults but not older adults. We investigated the OAB under two different task conditions. In Experiment 1 young and older adults (who reported more recent experience with own than other-age faces) completed a match-to-sample task with young and older adult faces; only young adults showed an OAB. In Experiment 2 young and older adults completed an identity detection task in which we manipulated the identity strength of target and distracter identities by morphing each face with an average face in 20% steps. Accuracy increased with identity strength and facial age influenced older adults' (but not younger adults') strategy, but there was no evidence of an OAB. Collectively, these results suggest that the OAB depends on task demands and may be absent when searching for one identity. © 2014 The British Psychological Society.

  16. Multimodal recognition based on face and ear using local feature

    NASA Astrophysics Data System (ADS)

    Yang, Ruyin; Mu, Zhichun; Chen, Long; Fan, Tingyu

    2017-06-01

    The pose issue which may cause loss of useful information has always been a bottleneck in face and ear recognition. To address this problem, we propose a multimodal recognition approach based on face and ear using local feature, which is robust to large facial pose variations in the unconstrained scene. Deep learning method is used for facial pose estimation, and the method of a well-trained Faster R-CNN is used to detect and segment the region of face and ear. Then we propose a weighted region-based recognition method to deal with the local feature. The proposed method has achieved state-of-the-art recognition performance especially when the images are affected by pose variations and random occlusion in unconstrained scene.

  17. Individual differences in cortical face selectivity predict behavioral performance in face recognition

    PubMed Central

    Huang, Lijie; Song, Yiying; Li, Jingguang; Zhen, Zonglei; Yang, Zetian; Liu, Jia

    2014-01-01

    In functional magnetic resonance imaging studies, object selectivity is defined as a higher neural response to an object category than other object categories. Importantly, object selectivity is widely considered as a neural signature of a functionally-specialized area in processing its preferred object category in the human brain. However, the behavioral significance of the object selectivity remains unclear. In the present study, we used the individual differences approach to correlate participants' face selectivity in the face-selective regions with their behavioral performance in face recognition measured outside the scanner in a large sample of healthy adults. Face selectivity was defined as the z score of activation with the contrast of faces vs. non-face objects, and the face recognition ability was indexed as the normalized residual of the accuracy in recognizing previously-learned faces after regressing out that for non-face objects in an old/new memory task. We found that the participants with higher face selectivity in the fusiform face area (FFA) and the occipital face area (OFA), but not in the posterior part of the superior temporal sulcus (pSTS), possessed higher face recognition ability. Importantly, the association of face selectivity in the FFA and face recognition ability cannot be accounted for by FFA response to objects or behavioral performance in object recognition, suggesting that the association is domain-specific. Finally, the association is reliable, confirmed by the replication from another independent participant group. In sum, our finding provides empirical evidence on the validity of using object selectivity as a neural signature in defining object-selective regions in the human brain. PMID:25071513

  18. Sparse representation based face recognition using weighted regions

    NASA Astrophysics Data System (ADS)

    Bilgazyev, Emil; Yeniaras, E.; Uyanik, I.; Unan, Mahmut; Leiss, E. L.

    2013-12-01

    Face recognition is a challenging research topic, especially when the training (gallery) and recognition (probe) images are acquired using different cameras under varying conditions. Even a small noise or occlusion in the images can compromise the accuracy of recognition. Lately, sparse encoding based classification algorithms gave promising results for such uncontrollable scenarios. In this paper, we introduce a novel methodology by modeling the sparse encoding with weighted patches to increase the robustness of face recognition even further. In the training phase, we define a mask (i.e., weight matrix) using a sparse representation selecting the facial regions, and in the recognition phase, we perform comparison on selected facial regions. The algorithm was evaluated both quantitatively and qualitatively using two comprehensive surveillance facial image databases, i.e., SCfaceandMFPV, with the results clearly superior to common state-of-the-art methodologies in different scenarios.

  19. A face recognition algorithm based on thermal and visible data

    NASA Astrophysics Data System (ADS)

    Sochenkov, Ilya; Tihonkih, Dmitrii; Vokhmintcev, Aleksandr; Melnikov, Andrey; Makovetskii, Artyom

    2016-09-01

    In this work we present an algorithm of fusing thermal infrared and visible imagery to identify persons. The proposed face recognition method contains several components. In particular this is rigid body image registration. The rigid registration is achieved by a modified variant of the iterative closest point (ICP) algorithm. We consider an affine transformation in three-dimensional space that preserves the angles between the lines. An algorithm of matching is inspirited by the recent results of neurophysiology of vision. Also we consider the ICP minimizing error metric stage for the case of an arbitrary affine transformation. Our face recognition algorithm also uses the localized-contouring algorithms to segment the subject's face; thermal matching based on partial least squares discriminant analysis. Thermal imagery face recognition methods are advantageous when there is no control over illumination or for detecting disguised faces. The proposed algorithm leads to good matching accuracies for different person recognition scenarios (near infrared, far infrared, thermal infrared, viewed sketch). The performance of the proposed face recognition algorithm in real indoor environments is presented and discussed.

  20. Face recognition with illumination and pose variations using MINACE filters

    NASA Astrophysics Data System (ADS)

    Casasent, David; Patnaik, Rohit

    2005-10-01

    This paper presents the status of our present CMU face recognition work. We first present a face recognition system that functions in the presence of illumination variations. We then present initial results when pose variations are also considered. A separate minimum noise and correlation energy (MINACE) filter is synthesized for each person. Our concern is face identification and impostor (non-database face) rejection. Most prior face identification did not address impostor rejection. We also present results for face verification with impostor rejection. The MINACE parameter c trades-off distortion-tolerance (recognition) versus discrimination (impostor rejection) performance. We use an automated filter-synthesis algorithm to select c and to synthesize the MINACE filter for each person using a training set of images of that person and a validation set of a few faces of other persons; this synthesis ensures both good recognition and impostor rejection performance. No impostor data is present in the training or validation sets. The peak-tocorrelation energy ratio (PCE) metric is used as the match-score in both the filter-synthesis and test stages and we show that it is better than use of the correlation peak value. We use circular correlations in filter synthesis and in tests, since such filters require one-fourth the storage space and similarly fewer on-line correlation calculations compared to the use of linear correlation filters. All training set images are registered (aligned) using the coordinates of several facial landmarks to remove scale variations and tilt bias. We also discuss the proper handling of pose variations by either pose estimation or by transforming the test input to all reference poses. Our face recognition system is evaluated using images from the CMU Pose, Illumination, and Expression (PIE) database. The same set of MINACE filters and impostor faces are used to evaluate the performance of the face identification and verification systems.

  1. [Neural basis of self-face recognition: social aspects].

    PubMed

    Sugiura, Motoaki

    2012-07-01

    Considering the importance of the face in social survival and evidence from evolutionary psychology of visual self-recognition, it is reasonable that we expect neural mechanisms for higher social-cognitive processes to underlie self-face recognition. A decade of neuroimaging studies so far has, however, not provided an encouraging finding in this respect. Self-face specific activation has typically been reported in the areas for sensory-motor integration in the right lateral cortices. This observation appears to reflect the physical nature of the self-face which representation is developed via the detection of contingency between one's own action and sensory feedback. We have recently revealed that the medial prefrontal cortex, implicated in socially nuanced self-referential process, is activated during self-face recognition under a rich social context where multiple other faces are available for reference. The posterior cingulate cortex has also exhibited this activation modulation, and in the separate experiment showed a response to attractively manipulated self-face suggesting its relevance to positive self-value. Furthermore, the regions in the right lateral cortices typically showing self-face-specific activation have responded also to the face of one's close friend under the rich social context. This observation is potentially explained by the fact that the contingency detection for physical self-recognition also plays a role in physical social interaction, which characterizes the representation of personally familiar people. These findings demonstrate that neuroscientific exploration reveals multiple facets of the relationship between self-face recognition and social-cognitive process, and that technically the manipulation of social context is key to its success.

  2. On the facilitative effects of face motion on face recognition and its development

    PubMed Central

    Xiao, Naiqi G.; Perrotta, Steve; Quinn, Paul C.; Wang, Zhe; Sun, Yu-Hao P.; Lee, Kang

    2014-01-01

    For the past century, researchers have extensively studied human face processing and its development. These studies have advanced our understanding of not only face processing, but also visual processing in general. However, most of what we know about face processing was investigated using static face images as stimuli. Therefore, an important question arises: to what extent does our understanding of static face processing generalize to face processing in real-life contexts in which faces are mostly moving? The present article addresses this question by examining recent studies on moving face processing to uncover the influence of facial movements on face processing and its development. First, we describe evidence on the facilitative effects of facial movements on face recognition and two related theoretical hypotheses: the supplementary information hypothesis and the representation enhancement hypothesis. We then highlight several recent studies suggesting that facial movements optimize face processing by activating specific face processing strategies that accommodate to task requirements. Lastly, we review the influence of facial movements on the development of face processing in the first year of life. We focus on infants' sensitivity to facial movements and explore the facilitative effects of facial movements on infants' face recognition performance. We conclude by outlining several future directions to investigate moving face processing and emphasize the importance of including dynamic aspects of facial information to further understand face processing in real-life contexts. PMID:25009517

  3. Face recognition under variable illumination via sparse representation of patches

    NASA Astrophysics Data System (ADS)

    Fan, Shouke; Liu, Rui; Feng, Weiguo; Zhu, Ming

    2013-10-01

    The objective of this work is to recognize faces under variations in illumination. Previous works have indicated that the variations in illumination can dramatically reduce the performance of face recognition. To this end - ;an efficient method for face recognition which is robust under variable illumination is proposed in this paper. First of all, a discrete cosine transform(DCT) in the logarithm domain is employed to preprocess the images, removing the illumination variations by discarding an appropriate number of low-frequency DCT coefficients. Then, a face image is partitioned into several patches, and we classify the patches using Sparse Representation-based Classification, respectively. At last, the identity of a test image can be determined by the classification results of its patches. Experimental results on the Yale B database and the CMU PIE database show that excellent recognition rates can be achieved by the proposed method.

  4. Uniform design based SVM model selection for face recognition

    NASA Astrophysics Data System (ADS)

    Li, Weihong; Liu, Lijuan; Gong, Weiguo

    2010-02-01

    Support vector machine (SVM) has been proved to be a powerful tool for face recognition. The generalization capacity of SVM depends on the model with optimal hyperparameters. The computational cost of SVM model selection results in application difficulty in face recognition. In order to overcome the shortcoming, we utilize the advantage of uniform design--space filling designs and uniformly scattering theory to seek for optimal SVM hyperparameters. Then we propose a face recognition scheme based on SVM with optimal model which obtained by replacing the grid and gradient-based method with uniform design. The experimental results on Yale and PIE face databases show that the proposed method significantly improves the efficiency of SVM model selection.

  5. LibME-automatic extraction of 3D ligand-binding motifs for mechanistic analysis of protein-ligand recognition.

    PubMed

    He, Wei; Liang, Zhi; Teng, MaiKun; Niu, LiWen

    2016-12-01

    Identifying conserved binding motifs is an efficient way to study protein-ligand recognition. Most 3D binding motifs only contain information from the protein side, and so motifs that combine information from both protein and ligand sides are desired. Here, we propose an algorithm called LibME (Ligand-binding Motif Extractor), which automatically extracts 3D binding motifs composed of the target ligand and surrounding conserved residues. We show that the motifs extracted by LibME for ATP and its analogs are highly similar to well-known motifs reported by previous studies. The superiority of our method to handle flexible ligands was also demonstrated using isocitric acid as an example. Finally, we show that these motifs, together with their visual exhibition, permit better investigating and understanding of protein-ligand recognition process.

  6. Facilitation of face recognition through the retino-tectal pathway.

    PubMed

    Nakano, Tamami; Higashida, Noriko; Kitazawa, Shigeru

    2013-08-01

    Humans can shift their gazes faster to human faces than to non-face targets during a task in which they are required to choose between face and non-face targets. However, it remains unclear whether a direct projection from the retina to the superior colliculus is specifically involved in this facilitated recognition of faces. To address this question, we presented a pair of face and non-face pictures to participants modulated in greyscale (luminance-defined stimuli) in one condition and modulated in a blue-yellow scale (S-cone-isolating stimuli) in another. The information of the S-cone-isolating stimuli is conveyed through the retino-geniculate pathway rather than the retino-tectal pathway. For the luminance stimuli, the reaction time was shorter towards a face than towards a non-face target. The facilitatory effect while choosing a face disappeared with the S-cone stimuli. Moreover, fearful faces elicited a significantly larger facilitatory effect relative to neutral faces, when the face (with or without emotion) and non-face stimuli were presented in greyscale. The effect of emotional expressions disappeared with the S-cone stimuli. In contrast to the S-cone stimuli, the face facilitatory effect was still observed with negated stimuli that were prepared by reversing the polarity of the original colour pictures and looked as unusual as the S-cone stimuli but still contained luminance information. These results demonstrate that the face facilitatory effect requires the facial and emotional information defined by luminance, suggesting that the luminance information conveyed through the retino-tectal pathway is responsible for the faster recognition of human faces.

  7. Robust Point Set Matching for Partial Face Recognition.

    PubMed

    Weng, Renliang; Lu, Jiwen; Tan, Yap-Peng

    2016-03-01

    Over the past three decades, a number of face recognition methods have been proposed in computer vision, and most of them use holistic face images for person identification. In many real-world scenarios especially some unconstrained environments, human faces might be occluded by other objects, and it is difficult to obtain fully holistic face images for recognition. To address this, we propose a new partial face recognition approach to recognize persons of interest from their partial faces. Given a pair of gallery image and probe face patch, we first detect keypoints and extract their local textural features. Then, we propose a robust point set matching method to discriminatively match these two extracted local feature sets, where both the textural information and geometrical information of local features are explicitly used for matching simultaneously. Finally, the similarity of two faces is converted as the distance between these two aligned feature sets. Experimental results on four public face data sets show the effectiveness of the proposed approach.

  8. Long-term effects of covert face recognition.

    PubMed

    Jenkins, Rob; Burton, A Mike; Ellis, Andrew W

    2002-12-01

    Covert face recognition has previously been thought to produce only very short-lasting effects. In this study we demonstrate that manipulating subjects' attentional load affects explicit, but not implicit memory for faces, and that implicit effects can persist over much longer intervals than is normally reported. Subjects performed letter-string tasks of high vs. low perceptual load (Lavie, N. (1995). Perceptual load as a necessary condition for selective attention. Journal of Experimental Psychology: Human Perception and Perfomance. 21, 451-468.), while ignoring task-irrelevant celebrity faces. Memory for the faces was then assessed using (a) a surprise recognition test for the celebrities' names, and (b) repetition priming in a face familiarity task. The load manipulation strongly influenced explicit recognition memory, but had no effect on repetition priming from the same items. Moreover, faces from the high load condition produced the same amount of priming whether they were explicitly remembered or not. This result resolves a long-standing anomaly in the face recognition literature, and is discussed in relation to covert processing in prosopagnosia.

  9. A Benchmark and Comparative Study of Video-Based Face Recognition on COX Face Database.

    PubMed

    Huang, Zhiwu; Shan, Shiguang; Wang, Ruiping; Zhang, Haihong; Lao, Shihong; Kuerban, Alifu; Chen, Xilin

    2015-12-01

    Face recognition with still face images has been widely studied, while the research on video-based face recognition is inadequate relatively, especially in terms of benchmark datasets and comparisons. Real-world video-based face recognition applications require techniques for three distinct scenarios: 1) Videoto-Still (V2S); 2) Still-to-Video (S2V); and 3) Video-to-Video (V2V), respectively, taking video or still image as query or target. To the best of our knowledge, few datasets and evaluation protocols have benchmarked for all the three scenarios. In order to facilitate the study of this specific topic, this paper contributes a benchmarking and comparative study based on a newly collected still/video face database, named COX(1) Face DB. Specifically, we make three contributions. First, we collect and release a largescale still/video face database to simulate video surveillance with three different video-based face recognition scenarios (i.e., V2S, S2V, and V2V). Second, for benchmarking the three scenarios designed on our database, we review and experimentally compare a number of existing set-based methods. Third, we further propose a novel Point-to-Set Correlation Learning (PSCL) method, and experimentally show that it can be used as a promising baseline method for V2S/S2V face recognition on COX Face DB. Extensive experimental results clearly demonstrate that video-based face recognition needs more efforts, and our COX Face DB is a good benchmark database for evaluation.

  10. Recognition of face and non-face stimuli in autistic spectrum disorder.

    PubMed

    Arkush, Leo; Smith-Collins, Adam P R; Fiorentini, Chiara; Skuse, David H

    2013-12-01

    The ability to remember faces is critical for the development of social competence. From childhood to adulthood, we acquire a high level of expertise in the recognition of facial images, and neural processes become dedicated to sustaining competence. Many people with autism spectrum disorder (ASD) have poor face recognition memory; changes in hairstyle or other non-facial features in an otherwise familiar person affect their recollection skills. The observation implies that they may not use the configuration of the inner face to achieve memory competence, but bolster performance in other ways. We aimed to test this hypothesis by comparing the performance of a group of high-functioning unmedicated adolescents with ASD and a matched control group on a "surprise" face recognition memory task. We compared their memory for unfamiliar faces with their memory for images of houses. To evaluate the role that is played by peripheral cues in assisting recognition memory, we cropped both sets of pictures, retaining only the most salient central features. ASD adolescents had poorer recognition memory for faces than typical controls, but their recognition memory for houses was unimpaired. Cropping images of faces did not disproportionately influence their recall accuracy, relative to controls. House recognition skills (cropped and uncropped) were similar in both groups. In the ASD group only, performance on both sets of task was closely correlated, implying that memory for faces and other complex pictorial stimuli is achieved by domain-general (non-dedicated) cognitive mechanisms. Adolescents with ASD apparently do not use domain-specialized processing of inner facial cues to support face recognition memory.

  11. Face Recognition From One Example View.

    DTIC Science & Technology

    1995-09-01

    From One Example View David Beymer and Tomaso Poggio email: beymer@ai.mit.edu, tp@ai.mit.edu Abstract To create a pose-invariant face recognizer, one...the example view. While this brings the two views into coarse alignment, small pose or expressional di erences may remain. To bring the input and

  12. 3D modeling and curvature analysis of orthogonal face gear considering the rounding tooth top of shaper cutter

    NASA Astrophysics Data System (ADS)

    Sun, Y. G.; Zhao, X. H.; Zhao, J.; Li, F. L.

    2017-09-01

    Aiming at the lack of the theory of transitional surface of face gear, the tooth profile equation of the orthogonal face gear is derived according to the differential geometry and meshing principle; In this paper, a tooth surface structure is proposed, which is a smooth surface of the tooth root transition part. A method of generating transition surface is presented, which takes into account the rounding tooth top of gear shaper cutter; the smooth transition surface equation of face gear tooth root is derived. The 3D solid model of the orthogonal face gear generated by two kinds of structures, the top angle of the gear shaper cutter and the addendum of the gear shaper cutter are established. The curvature analysis shows that the radius of curvature of the orthogonal face gear cut by the rounding tooth top of shaper cutter is larger than the other. The influence of the main parameters on the surface curvature of the face gear transmission is analyzed, the conclusion has reference value for the strength calculation and analysis of the face gear transmission.

  13. Can massive but passive exposure to faces contribute to face recognition abilities?

    PubMed

    Yovel, Galit; Halsband, Keren; Pelleg, Michel; Farkash, Naomi; Gal, Bracha; Goshen-Gottstein, Yonatan

    2012-04-01

    Recent studies have suggested that individuation of other-race faces is more crucial for enhancing recognition performance than exposure that involves categorization of these faces to an identity-irrelevant criterion. These findings were primarily based on laboratory training protocols that dissociated exposure and individuation by using categorization tasks. However, the absence of enhanced recognition following categorization may not simulate key aspects of real-life massive exposure without individuation to other-race faces. Real-life exposure spans years of seeing a multitude of faces, under variant conditions, including expression, view, lighting and gaze, albeit with no subcategory individuation. However, in most real-life settings, massive exposure operates in concert with individuation. An exception to that are neonatology nurses, a unique population that is exposed to--but do not individuate--massive numbers of newborn faces. Our findings show that recognition of newborn faces by nurses does not differ from adults who are rarely exposed to newborn faces. A control study showed that the absence of enhanced recognition cannot be attributed to the relatively short exposure to each newborn face in the neonatology unit or to newborns' apparent homogeneous appearance. It is therefore the quality--not the quantity--of exposure that determines recognition abilities.

  14. Equivalent activation of the hippocampus by face-face and face-laugh paired associate learning and recognition.

    PubMed

    Holdstock, J S; Crane, J; Bachorowski, J-A; Milner, B

    2010-11-01

    The human hippocampus is known to play an important role in relational memory. Both patient lesion studies and functional-imaging studies have shown that it is involved in the encoding and retrieval from memory of arbitrary associations. Two recent patient lesion studies, however, have found dissociations between spared and impaired memory within the domain of relational memory. Recognition of associations between information of the same kind (e.g., two faces) was spared, whereas recognition of associations between information of different kinds (e.g., face-name or face-voice associations) was impaired by hippocampal lesions. Thus, recognition of associations between information of the same kind may not be mediated by the hippocampus. Few imaging studies have directly compared activation at encoding and recognition of associations between same and different types of information. Those that have have shown mixed findings and been open to alternative interpretation. We used fMRI to compare hippocampal activation while participants studied and later recognized face-face and face-laugh paired associates. We found no differences in hippocampal activation between our two types of stimulus materials during either study or recognition. Study of both types of paired associate activated the hippocampus bilaterally, but the hippocampus was not activated by either condition during recognition. Our findings suggest that the human hippocampus is normally engaged to a similar extent by study and recognition of associations between information of the same kind and associations between information of different kinds.

  15. Analysis of 3D face forms for proper sizing and CAD of spectacle frames.

    PubMed

    Kouchi, Makiko; Mochimaru, Masaaki

    2004-11-01

    Three-dimensional morphological variations in the human face were analysed using digital models of the human face, and the usefulness of such analysis in designing industrial products was demonstrated by validating spectacle frame designs based on an original sizing system developed based on the analysis. A normalized model of the three-dimensional face form was made for each of 56 young adult Japanese males. The morphological distances between subjects were defined, and subjects were divided into four groups based on analysis of the distance matrix. A prototype spectacle frame was designed for the average form of each of the four groups. Tightening force of the prototype frames was adjusted using the materialized average forms with soft material placed at the nasal bridge and side of the head. Four prototype frames as well as a conventional frame were evaluated using sensory evaluation and physical measurement of the pressure and slip in 38 young adult male subjects. For each of the 38 subjects, prototype frames were ranked according to the morphological similarity of the subjects and the average form of the four groups: the frame designed for the average form of the group most similar to the subject was #1, the frame designed for the average form of the next most similar group was #2, and so on. For the groups with smaller or narrower faces, new frame #1 was most preferred and had the best overall fit, smallest slip sensation and largest pressure sensation. The groups with larger or wider faces preferred tighter frames than new frame #1, because they were concerned that the frames might slip, although the frames did not. Most of the subjects habitually wore spectacles, and the reason that groups with larger or wider faces preferred tighter frames was thought to be that they were accustomed to tighter fitting frames.

  16. Infrared and visible image fusion for face recognition

    NASA Astrophysics Data System (ADS)

    Singh, Saurabh; Gyaourova, Aglika; Bebis, George; Pavlidis, Ioannis

    2004-08-01

    Considerable progress has been made in face recognition research over the last decade especially with the development of powerful models of face appearance (i.e., eigenfaces). Despite the variety of approaches and tools studied, however, face recognition is not accurate or robust enough to be deployed in uncontrolled environments. Recently, a number of studies have shown that infrared (IR) imagery offers a promising alternative to visible imagery due to its relative insensitive to illumination changes. However, IR has other limitations including that it is opaque to glass. As a result, IR imagery is very sensitive to facial occlusion caused by eyeglasses. In this paper, we propose fusing IR with visible images, exploiting the relatively lower sensitivity of visible imagery to occlusions caused by eyeglasses. Two different fusion schemes have been investigated in this study: (1) image-based fusion performed in the wavelet domain and, (2) feature-based fusion performed in the eigenspace domain. In both cases, we employ Genetic Algorithms (GAs) to find an optimum strategy to perform the fusion. To evaluate and compare the proposed fusion schemes, we have performed extensive recognition experiments using the Equinox face dataset and the popular method of eigenfaces. Our results show substantial improvements in recognition performance overall, suggesting that the idea of fusing IR with visible images for face recognition deserves further consideration.

  17. Arguments Against a Configural Processing Account of Familiar Face Recognition.

    PubMed

    Burton, A Mike; Schweinberger, Stefan R; Jenkins, Rob; Kaufmann, Jürgen M

    2015-07-01

    Face recognition is a remarkable human ability, which underlies a great deal of people's social behavior. Individuals can recognize family members, friends, and acquaintances over a very large range of conditions, and yet the processes by which they do this remain poorly understood, despite decades of research. Although a detailed understanding remains elusive, face recognition is widely thought to rely on configural processing, specifically an analysis of spatial relations between facial features (so-called second-order configurations). In this article, we challenge this traditional view, raising four problems: (1) configural theories are underspecified; (2) large configural changes leave recognition unharmed; (3) recognition is harmed by nonconfigural changes; and (4) in separate analyses of face shape and face texture, identification tends to be dominated by texture. We review evidence from a variety of sources and suggest that failure to acknowledge the impact of familiarity on facial representations may have led to an overgeneralization of the configural account. We argue instead that second-order configural information is remarkably unimportant for familiar face recognition. © The Author(s) 2015.

  18. Method for secure electronic voting system: face recognition based approach

    NASA Astrophysics Data System (ADS)

    Alim, M. Affan; Baig, Misbah M.; Mehboob, Shahzain; Naseem, Imran

    2017-06-01

    In this paper, we propose a framework for low cost secure electronic voting system based on face recognition. Essentially Local Binary Pattern (LBP) is used for face feature characterization in texture format followed by chi-square distribution is used for image classification. Two parallel systems are developed based on smart phone and web applications for face learning and verification modules. The proposed system has two tire security levels by using person ID followed by face verification. Essentially class specific threshold is associated for controlling the security level of face verification. Our system is evaluated three standard databases and one real home based database and achieve the satisfactory recognition accuracies. Consequently our propose system provides secure, hassle free voting system and less intrusive compare with other biometrics.

  19. Real-time human face recognition system with high parallelism

    NASA Astrophysics Data System (ADS)

    Feng, Wenyi; He, Qingsheng; Yan, Yingbai; Jin, Guofan; Wu, Minxian

    1999-10-01

    Volume holographic associative storage in a photorefractive crystal provides an inherent mechanism to develop a multichannel correlation system for real-time human face recognition with high parallelism. Wavelet transform is introduced to improve parallelism and discrimination of the system. Parameters of the system are optimized for maximum parallelism under limitation of hardware in this paper. Two factors mainly relative to parallelism of the system, dynamic scanning scope of the reference beam and angle interval of the 2D scanning setup, are analyzed. In our experiments, correlation outputs between an input human face and hundreds of face templates are obtained instantly in parallel. It can be recognized by simply identifying position of the correlation peak with highest intensity. Invariance of the system for human face recognition is also studied. A novel method to recognize an input human face of any rotation angle is proposed and testified by experiments.

  20. Recognition of own-race and other-race caricatures: implications for models of face recognition.

    PubMed

    Byatt, G; Rhodes, G

    1998-08-01

    Valentine's (Valentine T. Q J Exp Psychol 1991;43A:161-204) face recognition framework supports both a norm-based coding (NBC) and an exemplar-only, absolute coding, model (ABC). According to NBC; (1) faces are represented in terms of deviations from a prototype or norm; (2) caricatures are effective because they exaggerate this norm deviation information; and (3) other-race faces are coded relative to the (only available) own-race norm. Therefore NBC predicts that, for European subjects, caricatures of Chinese faces made by distorting differences from the European norm would be more effective than caricatures made relative to the Chinese norm. According to ABC; (1) faces are encoded as absolute values on a set of shared dimensions with the norm playing no role in recognition; (2) caricatures are effective because they minimise exemplar density and (3) the dimensions of face-space are inappropriate for other-race faces leaving them relatively densely clustered. ABC predicts that all faces would be recognised more accurately when caricatured against their own-race norm. We tested European subjects' identification of European and Chinese faces, caricatured against both race norms. The ABC model's prediction was supported. European faces were also rated as more distinctive and recognised more easily than Chinese faces. However, the own-race recognition bias held even when the races were equated for distinctiveness which suggests that the ABC model may not provide a complete account of race effects in recognition.

  1. Face recognition using facial expression: a novel approach

    NASA Astrophysics Data System (ADS)

    Singh, Deepak Kumar; Gupta, Priya; Tiwary, U. S.

    2008-04-01

    Facial expressions are undoubtedly the most effective nonverbal communication. The face has always been the equation of a person's identity. The face draws the demarcation line between identity and extinction. Each line on the face adds an attribute to the identity. These lines become prominent when we experience an emotion and these lines do not change completely with age. In this paper we have proposed a new technique for face recognition which focuses on the facial expressions of the subject to identify his face. This is a grey area on which not much light has been thrown earlier. According to earlier researches it is difficult to alter the natural expression. So our technique will be beneficial for identifying occluded or intentionally disguised faces. The test results of the experiments conducted prove that this technique will give a new direction in the field of face recognition. This technique will provide a strong base to the area of face recognition and will be used as the core method for critical defense security related issues.

  2. Thermal Face Recognition in an Operational Scenario

    DTIC Science & Technology

    2004-01-01

    were based on gallery and probe sets collected in- doors during a single session . In that respect, they resemble the fa/fb tests in the FERET program...part by the DARPA Human Identifica- tion at a Distance (HID) program, contract # DARPA/AFOSR F49620-01- C-0008. imagery collected in a single session . Their...during a single session . During data collection illumination condi- tions were purposely varied in order to present a challenge for visible face

  3. Evaluation of Model Recognition for Grammar-Based Automatic 3d Building Model Reconstruction

    NASA Astrophysics Data System (ADS)

    Yu, Qian; Helmholz, Petra; Belton, David

    2016-06-01

    In recent years, 3D city models are in high demand by many public and private organisations, and the steadily growing capacity in both quality and quantity are increasing demand. The quality evaluation of these 3D models is a relevant issue both from the scientific and practical points of view. In this paper, we present a method for the quality evaluation of 3D building models which are reconstructed automatically from terrestrial laser scanning (TLS) data based on an attributed building grammar. The entire evaluation process has been performed in all the three dimensions in terms of completeness and correctness of the reconstruction. Six quality measures are introduced to apply on four datasets of reconstructed building models in order to describe the quality of the automatic reconstruction, and also are assessed on their validity from the evaluation point of view.

  4. A new-generation 3D ozone FACE (Free Air Controlled Exposure).

    PubMed

    Paoletti, Elena; Materassi, Alessandro; Fasano, Gianni; Hoshika, Yasutomo; Carriero, Giulia; Silaghi, Diana; Badea, Ovidiu

    2017-01-01

    To artificially simulate the impacts of ground-level ozone (O3) on vegetation, ozone FACE (Free Air Controlled Exposure) systems are increasingly recommended. We describe here a new-generation, three-dimensional ozone FACE, with O3 diffusion through laser-generated micro-holes, pre-mixing of air and O3, O3 generator with integral oxygen generator, continuous (day/night) exposure and full replication. Based on three O3 levels and assumptions on the pre-industrial O3 levels, we describe principles to calculate relative yield/biomass and estimate impacts even at lower-than-ambient O3 levels. The case study is called FO3X, and is at present the only ozone FACE in Mediterranean climate and one of the very few ozone FACEs investigating more than one stressor at a time. The results presented here will give further impulse to the research on O3 impacts on vegetation all over the world. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. Pose Invariant Face Recognition Based on Hybrid Dominant Frequency Features

    NASA Astrophysics Data System (ADS)

    Wijaya, I. Gede Pasek Suta; Uchimura, Keiichi; Hu, Zhencheng

    Face recognition is one of the most active research areas in pattern recognition, not only because the face is a human biometric characteristics of human being but also because there are many potential applications of the face recognition which range from human-computer interactions to authentication, security, and surveillance. This paper presents an approach to pose invariant human face image recognition. The proposed scheme is based on the analysis of discrete cosine transforms (DCT) and discrete wavelet transforms (DWT) of face images. From both the DCT and DWT domain coefficients, which describe the facial information, we build compact and meaningful features vector, using simple statistical measures and quantization. This feature vector is called as the hybrid dominant frequency features. Then, we apply a combination of the L2 and Lq metric to classify the hybrid dominant frequency features to a person's class. The aim of the proposed system is to overcome the high memory space requirement, the high computational load, and the retraining problems of previous methods. The proposed system is tested using several face databases and the experimental results are compared to a well-known Eigenface method. The proposed method shows good performance, robustness, stability, and accuracy without requiring geometrical normalization. Furthermore, the purposed method has low computational cost, requires little memory space, and can overcome retraining problem.

  6. A wavelet-based method for multispectral face recognition

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng; Zhang, Chaoyang; Zhou, Zhaoxian

    2012-06-01

    A wavelet-based method is proposed for multispectral face recognition in this paper. Gabor wavelet transform is a common tool for orientation analysis of a 2D image; whereas Hamming distance is an efficient distance measurement for face identification. Specifically, at each frequency band, an index number representing the strongest orientational response is selected, and then encoded in binary format to favor the Hamming distance calculation. Multiband orientation bit codes are then organized into a face pattern byte (FPB) by using order statistics. With the FPB, Hamming distances are calculated and compared to achieve face identification. The FPB algorithm was initially created using thermal images, while the EBGM method was originated with visible images. When two or more spectral images from the same subject are available, the identification accuracy and reliability can be enhanced using score fusion. We compare the identification performance of applying five recognition algorithms to the three-band (visible, near infrared, thermal) face images, and explore the fusion performance of combing the multiple scores from three recognition algorithms and from three-band face images, respectively. The experimental results show that the FPB is the best recognition algorithm, the HMM yields the best fusion result, and the thermal dataset results in the best fusion performance compared to other two datasets.

  7. Wavelet-based illumination invariant preprocessing in face recognition

    NASA Astrophysics Data System (ADS)

    Goh, Yi Zheng; Teoh, Andrew Beng Jin; Goh, Kah Ong Michael

    2009-04-01

    Performance of a contemporary two-dimensional face-recognition system has not been satisfied due to the variation in lighting. As a result, many works of solving illumination variation in face recognition have been carried out in past decades. Among them, the Illumination-Reflectance model is one of the generic models that is used to separate the individual reflectance and illumination components of an object. The illumination component can be removed by means of image-processing techniques to regain an intrinsic face feature, which is depicted by the reflectance component. We present a wavelet-based illumination invariant algorithm as a preprocessing technique for face recognition. On the basis of the multiresolution nature of wavelet analysis, we decompose both illumination and reflectance components from a face image in a systematic way. The illumination component wherein resides in the low-spatial-frequency subband can be eliminated efficiently. This technique works out very advantageously for achieving higher recognition performance on YaleB, CMU PIE, and FRGC face databases.

  8. Face Recognition by Metropolitan Police Super-Recognisers

    PubMed Central

    Robertson, David J.; Noyes, Eilidh; Dowsett, Andrew J.; Jenkins, Rob; Burton, A. Mike

    2016-01-01

    Face recognition is used to prove identity across a wide variety of settings. Despite this, research consistently shows that people are typically rather poor at matching faces to photos. Some professional groups, such as police and passport officers, have been shown to perform just as poorly as the general public on standard tests of face recognition. However, face recognition skills are subject to wide individual variation, with some people showing exceptional ability—a group that has come to be known as ‘super-recognisers’. The Metropolitan Police Force (London) recruits ‘super-recognisers’ from within its ranks, for deployment on various identification tasks. Here we test four working super-recognisers from within this police force, and ask whether they are really able to perform at levels above control groups. We consistently find that the police ‘super-recognisers’ perform at well above normal levels on tests of unfamiliar and familiar face matching, with degraded as well as high quality images. Recruiting employees with high levels of skill in these areas, and allocating them to relevant tasks, is an efficient way to overcome some of the known difficulties associated with unfamiliar face recognition. PMID:26918457

  9. Face Recognition by Metropolitan Police Super-Recognisers.

    PubMed

    Robertson, David J; Noyes, Eilidh; Dowsett, Andrew J; Jenkins, Rob; Burton, A Mike

    2016-01-01

    Face recognition is used to prove identity across a wide variety of settings. Despite this, research consistently shows that people are typically rather poor at matching faces to photos. Some professional groups, such as police and passport officers, have been shown to perform just as poorly as the general public on standard tests of face recognition. However, face recognition skills are subject to wide individual variation, with some people showing exceptional ability-a group that has come to be known as 'super-recognisers'. The Metropolitan Police Force (London) recruits 'super-recognisers' from within its ranks, for deployment on various identification tasks. Here we test four working super-recognisers from within this police force, and ask whether they are really able to perform at levels above control groups. We consistently find that the police 'super-recognisers' perform at well above normal levels on tests of unfamiliar and familiar face matching, with degraded as well as high quality images. Recruiting employees with high levels of skill in these areas, and allocating them to relevant tasks, is an efficient way to overcome some of the known difficulties associated with unfamiliar face recognition.

  10. Eye contrast polarity is critical for face recognition by infants.

    PubMed

    Otsuka, Yumiko; Motoyoshi, Isamu; Hill, Harold C; Kobayashi, Megumi; Kanazawa, So; Yamaguchi, Masami K

    2013-07-01

    Just as faces share the same basic arrangement of features, with two eyes above a nose above a mouth, human eyes all share the same basic contrast polarity relations, with a sclera lighter than an iris and a pupil, and this is unique among primates. The current study examined whether this bright-dark relationship of sclera to iris plays a critical role in face recognition from early in development. Specifically, we tested face discrimination in 7- and 8-month-old infants while independently manipulating the contrast polarity of the eye region and of the rest of the face. This gave four face contrast polarity conditions: fully positive condition, fully negative condition, positive face with negated eyes ("negative eyes") condition, and negated face with positive eyes ("positive eyes") condition. In a familiarization and novelty preference procedure, we found that 7- and 8-month-olds could discriminate between faces only when the contrast polarity of the eyes was preserved (positive) and that this did not depend on the contrast polarity of the rest of the face. This demonstrates the critical role of eye contrast polarity for face recognition in 7- and 8-month-olds and is consistent with previous findings for adults. Copyright © 2013 Elsevier Inc. All rights reserved.

  11. Familiarity is not notoriety: phenomenological accounts of face recognition

    PubMed Central

    Liccione, Davide; Moruzzi, Sara; Rossi, Federica; Manganaro, Alessia; Porta, Marco; Nugrahaningsih, Nahumi; Caserio, Valentina; Allegri, Nicola

    2014-01-01

    From a phenomenological perspective, faces are perceived differently from objects as their perception always involves the possibility of a relational engagement (Bredlau, 2011). This is especially true for familiar faces, i.e., faces of people with a history of real relational engagements. Similarly, valence of emotional expressions assumes a key role, as they define the sense and direction of this engagement. Following these premises, the aim of the present study is to demonstrate that face recognition is facilitated by at least two variables, familiarity and emotional expression, and that perception of familiar faces is not influenced by orientation. In order to verify this hypothesis, we implemented a 3 × 3 × 2 factorial design, showing 17 healthy subjects three type of faces (unfamiliar, personally familiar, famous) characterized by three different emotional expressions (happy, hungry/sad, neutral) and in two different orientation (upright vs. inverted). We showed every subject a total of 180 faces with the instructions to give a familiarity judgment. Reaction times (RTs) were recorded and we found that the recognition of a face is facilitated by personal familiarity and emotional expression, and that this process is otherwise independent from a cognitive elaboration of stimuli and remains stable despite orientation. These results highlight the need to make a distinction between famous and personally familiar faces when studying face perception and to consider its historical aspects from a phenomenological point of view. PMID:25225476

  12. Faces are special but not too special: spared face recognition in amnesia is based on familiarity.

    PubMed

    Aly, Mariam; Knight, Robert T; Yonelinas, Andrew P

    2010-11-01

    Most current theories of human memory are material-general in the sense that they assume that the medial temporal lobe (MTL) is important for retrieving the details of prior events, regardless of the specific type of materials. Recent studies of amnesia have challenged the material-general assumption by suggesting that the MTL may be necessary for remembering words, but is not involved in remembering faces. We examined recognition memory for faces and words in a group of amnesic patients, which included hypoxic patients and patients with extensive left or right MTL lesions. Recognition confidence judgments were used to plot receiver operating characteristics (ROCs) in order to more fully quantify recognition performance and to estimate the contributions of recollection and familiarity. Consistent with the extant literature, an analysis of overall recognition accuracy showed that the patients were impaired at word memory but had spared face memory. However, the ROC analysis indicated that the patients were generally impaired at high confidence recognition responses for faces and words, and they exhibited significant recollection impairments for both types of materials. Familiarity for faces was preserved in all patients, but extensive left MTL damage impaired familiarity for words. These results show that face recognition may appear to be spared because performance tends to rely heavily on familiarity, a process that is relatively well preserved in amnesia. In addition, the findings challenge material-general theories of memory, and suggest that both material and process are important determinants of memory performance in amnesia.

  13. The hows and whys of face memory: level of construal influences the recognition of human faces

    PubMed Central

    Wyer, Natalie A.; Hollins, Timothy J.; Pahl, Sabine; Roper, Jean

    2015-01-01

    Three experiments investigated the influence of level of construal (i.e., the interpretation of actions in terms of their meaning or their details) on different stages of face memory. We employed a standard multiple-face recognition paradigm, with half of the faces inverted at test. Construal level was manipulated prior to recognition (Experiment 1), during study (Experiment 2) or both (Experiment 3). The results support a general advantage for high-level construal over low-level construal at both study and at test, and suggest that matching processing style between study and recognition has no advantage. These experiments provide additional evidence in support of a link between semantic processing (i.e., construal) and visual (i.e., face) processing. We conclude with a discussion of implications for current theories relating to both construal and face processing. PMID:26500586

  14. Best Basis Selection Method Using Learning Weights for Face Recognition

    PubMed Central

    Lee, Wonju; Cheon, Minkyu; Hyun, Chang-Ho; Park, Mignon

    2013-01-01

    In the face recognition field, principal component analysis is essential to the reduction of the image dimension. In spite of frequent use of this analysis, it is commonly believed that the basis faces with large eigenvalues are chosen as the best subset in the nearest neighbor classifiers. We propose an alternative that can predict the classification error during the training steps and find the useful basis faces for the similarity metrics of the classical pattern algorithms. In addition, we also show the need for the eye-aligned dataset to have the pure face. The experiments using face images verify that our method reduces the negative effect on the misaligned face images and decreases the weights of the useful basis faces in order to improve the classification accuracy. PMID:24072026

  15. Survey of Commercial Technologies for Face Recognition in Video

    DTIC Science & Technology

    2014-09-01

    search facial components, identify a gestalt face 11 and compare it to a stored set of facial characteristics of known human faces. 3.2 Recognition System...theorize that a face is not merely a set of facial features but is rather something meaningful in its form. This is consistent with the Gestalt theory that...an image is seen in its entirety, not by its individual parts. Hence, the “ gestalt face” refers to a holistic representation of face. Gestalt’s theory

  16. Face Encoding and Recognition in the Human Brain

    NASA Astrophysics Data System (ADS)

    Haxby, James V.; Ungerleider, Leslie G.; Horwitz, Barry; Maisog, Jose Ma.; Rapoport, Stanley I.; Grady, Cheryl L.

    1996-01-01

    A dissociation between human neural systems that participate in the encoding and later recognition of new memories for faces was demonstrated by measuring memory task-related changes in regional cerebral blood flow with positron emission tomography. There was almost no overlap between the brain structures associated with these memory functions. A region in the right hippocampus and adjacent cortex was activated during memory encoding but not during recognition. The most striking finding in neocortex was the lateralization of prefrontal participation. Encoding activated left prefrontal cortex, whereas recognition activated right prefrontal cortex. These results indicate that the hippocampus and adjacent cortex participate in memory function primarily at the time of new memory encoding. Moreover, face recognition is not mediated simply by recapitulation of operations performed at the time of encoding but, rather, involves anatomically dissociable operations.

  17. A Novel Feature Vectors Construction Approach for Face Recognition

    NASA Astrophysics Data System (ADS)

    Nicholl, Paul; Ahmad, Afandi; Amira, Abbes

    This paper discusses a novel feature vectors construction approach for face recognition using discrete wavelet transform (DWT). Four experiments have been carried out focusing on: DWT feature selection, DWT filter choice, features optimization by coefficients selection as well as feature threshold. In order to explore the most suitable method of feature extraction, different wavelet quadrant and scales have been studied. It then followed with an evaluation of different wavelet filter choices and their impact on recognition accuracy. An approach for face recognition based on coefficient selection for DWT is the presented and analyzed. Moreover, a study has been deployed to investigate ways of selecting the DWT coefficient threshold. The results obtained using the AT&T database have shown a significant achievement over existing DWT/PCA coefficient selection techniques and the approach presented increases recognition accuracy from 94% to 97% when the Coiflet 3 wavelet is used.

  18. Learning from humans: computational modeling of face recognition.

    PubMed

    Wallraven, Christian; Schwaninger, Adrian; Bülthoff, Heinrich H

    2005-12-01

    In this paper, we propose a computational architecture of face recognition based on evidence from cognitive research. Several recent psychophysical experiments have shown that humans process faces by a combination of configural and component information. Using an appearance-based implementation of this architecture based on low-level features and their spatial relations, we were able to model aspects of human performance found in psychophysical studies. Furthermore, results from additional computational recognition experiments show that our framework is able to achieve excellent recognition performance even under large view rotations. Our interdisciplinary study is an example of how results from cognitive research can be used to construct recognition systems with increased performance. Finally, our modeling results also make new experimental predictions that will be tested in further psychophysical studies, thus effectively closing the loop between psychophysical experimentation and computational modeling.

  19. Nonlinear fusion for face recognition using fuzzy integral

    NASA Astrophysics Data System (ADS)

    Chen, Xuerong; Jing, Zhongliang; Xiao, Gang

    2007-08-01

    Face recognition based only on the visual spectrum is not accurate or robust enough to be used in uncontrolled environments. Recently, infrared (IR) imagery of human face is considered as a promising alternative to visible imagery due to its relative insensitive to illumination changes. However, IR has its own limitations. In order to fuse information from the two modalities to achieve better result, we propose a new fusion recognition scheme based on nonlinear decision fusion, using fuzzy integral to fuse the objective evidence supplied by each modality. The scheme also employs independent component analysis (ICA) for feature extraction and support vector machines (SVMs) for classification evidence. Recognition rate is used to evaluate the proposed scheme. Experimental results show the scheme improves recognition performance substantially.

  20. Numerical simulation of turbulent heat transfer past a backward-facing step: 2D/3D RANS versus IDDES solutions

    NASA Astrophysics Data System (ADS)

    Smirnov, E. M.; Smirnovsky, A. A.; Schur, N. A.; Zaitsev, D. K.; Smirnov, P. E.

    2016-09-01

    The contribution covers results of numerical study of air flow and heat transfer past a backward-facing step at the Reynolds number of 28,000. The numerical simulation was carried out under conditions of the experiments of Vogel&Eaton (1985), where nominally 2D fluid dynamics and heat transfer in a channel with expansion ratio of 1.25 was investigated. Two approaches were used for turbulence modelling. First, the Menter SST turbulence model was used to perform refined 2D and 3D RANS steady-state computations. The 3D analysis was undertaken to evaluate effects of boundary layers developing on the sidewalls of the experimental channel. Then, 3D time-dependent computations were carried out using the vortex-resolving IDDES method and applying the spanwise-periodicity conditions. Comparative computations were performed using an in-house finite-volume code SINF/Flag-S and the ANSYS Fluent. The codes produced practically identical RANS solutions, showing in particular a difference of 4% in the central-line peak Stanton number calculated in 2D and 3D cases. The IDDES results obtained with two codes are in a satisfactory agreement. Comparing with the experimental data, the IDDES produces the best agreement for the wall friction, whereas the RANS solutions show superiority in predictions of the local Stanton number distribution.

  1. Extraction and fusion of spectral parameters for face recognition

    NASA Astrophysics Data System (ADS)

    Boisier, B.; Billiot, B.; Abdessalem, Z.; Gouton, P.; Hardeberg, J. Y.

    2011-03-01

    Many methods have been developed in image processing for face recognition, especially in recent years with the increase of biometric technologies. However, most of these techniques are used on grayscale images acquired in the visible range of the electromagnetic spectrum. The aims of our study are to improve existing tools and to develop new methods for face recognition. The techniques used take advantage of the different spectral ranges, the visible, optical infrared and thermal infrared, by either combining them or analyzing them separately in order to extract the most appropriate information for face recognition. We also verify the consistency of several keypoints extraction techniques in the Near Infrared (NIR) and in the Visible Spectrum.

  2. Evidence for view-invariant face recognition units in unfamiliar face learning.

    PubMed

    Etchells, David B; Brooks, Joseph L; Johnston, Robert A

    2017-05-01

    Many models of face recognition incorporate the idea of a face recognition unit (FRU), an abstracted representation formed from each experience of a face which aids recognition under novel viewing conditions. Some previous studies have failed to find evidence of this FRU representation. Here, we report three experiments which investigated this theoretical construct by modifying the face learning procedure from that in previous work. During learning, one or two views of previously unfamiliar faces were shown to participants in a serial matching task. Later, participants attempted to recognize both seen and novel views of the learned faces (recognition phase). Experiment 1 tested participants' recognition of a novel view, a day after learning. Experiment 2 was identical, but tested participants on the same day as learning. Experiment 3 repeated Experiment 1, but tested participants on a novel view that was outside the rotation of those views learned. Results revealed a significant advantage, across all experiments, for recognizing a novel view when two views had been learned compared to single view learning. The observed view invariance supports the notion that an FRU representation is established during multi-view face learning under particular learning conditions.

  3. Are portrait artists superior face recognizers? Limited impact of adult experience on face recognition ability.

    PubMed

    Tree, Jeremy J; Horry, Ruth; Riley, Howard; Wilmer, Jeremy B

    2017-04-01

    Across 2 studies, the authors asked whether extensive experience in portrait art is associated with face recognition ability. In Study 1, 64 students completed a standardized face recognition test before and after completing a year-long art course that included substantial portraiture training. They found no evidence of an improvement in face recognition after training over and above what would be expected by practice alone. In Study 2, the authors investigated the possibility that more extensive experience might be needed for such advantages to emerge, by testing a cohort of expert portrait artists (N = 28), all of whom had many years of experience. In addition to memory for faces, they also explored memory for abstract art and for words in a paired-associate recognition test. The expert portrait artists performed similarly to a large, normative comparison sample on memory for faces and words but showed a small advantage for abstract art. Taken together, the results converge with existing literature to suggest that there is relatively little plasticity in face recognition in adulthood, at which point our substantial everyday experience with faces may have pushed us to the limits of our capabilities. (PsycINFO Database Record

  4. CMOS sensor for face tracking and recognition

    NASA Astrophysics Data System (ADS)

    Ginhac, Dominique; Prasetyo, Eri; Paindavoine, Michel

    2005-03-01

    This paper describes the main principles of a vision sensor dedicated to the detecting and tracking faces in video sequences. For this purpose, a current mode CMOS active sensor has been designed using an array of pixels that are amplified by using current mirrors of column amplifier. This circuit is simulated using Mentor Graphics software with parameters of a 0.6 μm CMOS process. The circuit design is added with a sequential control unit which purpose is to realise capture of subwindows at any location and any size in the whole image.

  5. Recognition by Humans and Pigeons of Novel Views of 3-D Objects and Their Photographs

    ERIC Educational Resources Information Center

    Friedman, Alinda; Spetch, Marcia L.; Ferrey, Anne

    2005-01-01

    Humans and pigeons were trained to discriminate between 2 views of actual 3-D objects or their photographs. They were tested on novel views that were either within the closest rotational distance between the training views (interpolated) or outside of that range (extrapolated). When training views were 60? apart, pigeons, but not humans,…

  6. Recognition by Humans and Pigeons of Novel Views of 3-D Objects and Their Photographs

    ERIC Educational Resources Information Center

    Friedman, Alinda; Spetch, Marcia L.; Ferrey, Anne

    2005-01-01

    Humans and pigeons were trained to discriminate between 2 views of actual 3-D objects or their photographs. They were tested on novel views that were either within the closest rotational distance between the training views (interpolated) or outside of that range (extrapolated). When training views were 60? apart, pigeons, but not humans,…

  7. What types of visual recognition tasks are mediated by the neural subsystem that subserves face recognition?

    PubMed

    Brooks, Brian E; Cooper, Eric E

    2006-07-01

    Three divided visual field experiments tested current hypotheses about the types of visual shape representation tasks that recruit the cognitive and neural mechanisms underlying face recognition. Experiment 1 found a right hemisphere advantage for subordinate but not basic-level face recognition. Experiment 2 found a right hemisphere advantage for basic but not superordinate-level animal recognition. Experiment 3 found that inverting animals eliminates the right hemisphere advantage for basic-level animal recognition. This pattern of results suggests that the cognitive and neural mechanisms underlying face recognition are recruited when computational demands of a shape representation task are best served through the use of coordinate (rather than categorical) spatial relations. Copyright 2006 APA, all rights reserved.

  8. Direct structural connections between voice- and face-recognition areas.

    PubMed

    Blank, Helen; Anwander, Alfred; von Kriegstein, Katharina

    2011-09-07

    Currently, there are two opposing models for how voice and face information is integrated in the human brain to recognize person identity. The conventional model assumes that voice and face information is only combined at a supramodal stage (Bruce and Young, 1986; Burton et al., 1990; Ellis et al., 1997). An alternative model posits that areas encoding voice and face information also interact directly and that this direct interaction is behaviorally relevant for optimizing person recognition (von Kriegstein et al., 2005; von Kriegstein and Giraud, 2006). To disambiguate between the two different models, we tested for evidence of direct structural connections between voice- and face-processing cortical areas by combining functional and diffusion magnetic resonance imaging. We localized, at the individual subject level, three voice-sensitive areas in anterior, middle, and posterior superior temporal sulcus (STS) and face-sensitive areas in the fusiform gyrus [fusiform face area (FFA)]. Using probabilistic tractography, we show evidence that the FFA is structurally connected with voice-sensitive areas in STS. In particular, our results suggest that the FFA is more strongly connected to middle and anterior than to posterior areas of the voice-sensitive STS. This specific structural connectivity pattern indicates that direct links between face- and voice-recognition areas could be used to optimize human person recognition.

  9. Toward Development of a Face Recognition System for Watchlist Surveillance.

    PubMed

    Kamgar-Parsi, Behrooz; Lawson, Wallace; Kamgar-Parsi, Behzad

    2011-10-01

    The interest in face recognition is moving toward real-world applications and uncontrolled sensing environments. An important application of interest is automated surveillance, where the objective is to recognize and track people who are on a watchlist. For this open world application, a large number of cameras that are increasingly being installed at many locations in shopping malls, metro systems, airports, etc., will be utilized. While a very large number of people will approach or pass by these surveillance cameras, only a small set of individuals must be recognized. That is, the system must reject every subject unless the subject happens to be on the watchlist. While humans routinely reject previously unseen faces as strangers, rejection of previously unseen faces has remained a difficult aspect of automated face recognition. In this paper, we propose an approach motivated by human perceptual ability of face recognition which can handle previously unseen faces. Our approach is based on identifying the decision region(s) in the face space which belong to the target person(s). This is done by generating two large sets of borderline images, projecting just inside and outside of the decision region. For each person on the watchlist, a dedicated classifier is trained. Results of extensive experiments support the effectiveness of our approach. In addition to extensive experiments using our algorithm and prerecorded images, we have conducted considerable live system experiments with people in realistic environments.

  10. Face recognition by using optical correlator with wavelet preprocessing

    NASA Astrophysics Data System (ADS)

    Strzelecki, Jacek; Chalasinska-Macukow, Katarzyna

    2004-08-01

    The method of face recognition by using optical correlator with wavelet preprocessing is presented. The wavelet transform is used to improve the performance of standard Vander Lugt correlator with phase only filter (POF). The influence of various wavelet transforms of images of human faces on the recognition results has been analyzed. The quality of the face recognition process was tested according to two criteria: the peak to correlation energy ratio (PCE), and the discrimination capability (DC). Additionally, proper localization of correlation peak has been controlled. During the preprocessing step a set of three wavelets -- mexican hat, Haar, and Gabor wavelets, with various scales was used. In addition, Gabor wavelets were tested for various orientation angles. During the recognition procedure the input scene and POF are transformed by the same wavelet. We show the results of the computer simulation for a variety of images of human faces: original images without any distortions, noisy images, and images with non-uniform light ilumination. A comparison of results of recognition obtained with and without wavelet preprocessing is given.

  11. Face recognition using composite classifier with 2DPCA

    NASA Astrophysics Data System (ADS)

    Li, Jia; Yan, Ding

    2017-01-01

    In the conventional face recognition, most researchers focused on enhancing the precision which input data was already the member of database. However, they paid less necessary attention to confirm whether the input data belonged to database. This paper proposed an approach of face recognition using two-dimensional principal component analysis (2DPCA). It designed a novel composite classifier founded by statistical technique. Moreover, this paper utilized the advantages of SVM and Logic Regression in field of classification and therefore made its accuracy improved a lot. To test the performance of the composite classifier, the experiments were implemented on the ORL and the FERET database and the result was shown and evaluated.

  12. Can Massive but Passive Exposure to Faces Contribute to Face Recognition Abilities?

    ERIC Educational Resources Information Center

    Yovel, Galit; Halsband, Keren; Pelleg, Michel; Farkash, Naomi; Gal, Bracha; Goshen-Gottstein, Yonatan

    2012-01-01

    Recent studies have suggested that individuation of other-race faces is more crucial for enhancing recognition performance than exposure that involves categorization of these faces to an identity-irrelevant criterion. These findings were primarily based on laboratory training protocols that dissociated exposure and individuation by using…

  13. Can Massive but Passive Exposure to Faces Contribute to Face Recognition Abilities?

    ERIC Educational Resources Information Center

    Yovel, Galit; Halsband, Keren; Pelleg, Michel; Farkash, Naomi; Gal, Bracha; Goshen-Gottstein, Yonatan

    2012-01-01

    Recent studies have suggested that individuation of other-race faces is more crucial for enhancing recognition performance than exposure that involves categorization of these faces to an identity-irrelevant criterion. These findings were primarily based on laboratory training protocols that dissociated exposure and individuation by using…

  14. Generating virtual training samples for sparse representation of face images and face recognition

    NASA Astrophysics Data System (ADS)

    Du, Yong; Wang, Yu

    2016-03-01

    There are many challenges in face recognition. In real-world scenes, images of the same face vary with changing illuminations, different expressions and poses, multiform ornaments, or even altered mental status. Limited available training samples cannot convey these possible changes in the training phase sufficiently, and this has become one of the restrictions to improve the face recognition accuracy. In this article, we view the multiplication of two images of the face as a virtual face image to expand the training set and devise a representation-based method to perform face recognition. The generated virtual samples really reflect some possible appearance and pose variations of the face. By multiplying a training sample with another sample from the same subject, we can strengthen the facial contour feature and greatly suppress the noise. Thus, more human essential information is retained. Also, uncertainty of the training data is simultaneously reduced with the increase of the training samples, which is beneficial for the training phase. The devised representation-based classifier uses both the original and new generated samples to perform the classification. In the classification phase, we first determine K nearest training samples for the current test sample by calculating the Euclidean distances between the test sample and training samples. Then, a linear combination of these selected training samples is used to represent the test sample, and the representation result is used to classify the test sample. The experimental results show that the proposed method outperforms some state-of-the-art face recognition methods.

  15. Dialog-Based 3D-Image Recognition Using a Domain Ontology

    NASA Astrophysics Data System (ADS)

    Hois, Joana; Wünstel, Michael; Bateman, John A.; Röfer, Thomas

    The combination of vision and speech, together with the resulting necessity for formal representations, builds a central component of an autonomous system. A robot that is supposed to navigate autonomously through space must be able to perceive its environment as automatically as possible. But each recognition system has its own inherent limits. Especially a robot whose task is to navigate through unknown terrain has to deal with unidentified or even unknown objects, thus compounding the recognition problem still further. The system described in this paper takes this into account by trying to identify objects based on their functionality where possible. To handle cases where recognition is insufficient, we examine here two further strategies: on the one hand, the linguistic reference and labeling of the unidentified objects and, on the other hand, ontological deduction. This approach then connects the probabilistic area of object recognition with the logical area of formal reasoning. In order to support formal reasoning, additional relational scene information has to be supplied by the recognition system. Moreover, for a sound ontological basis for these reasoning tasks, it is necessary to define a domain ontology that provides for the representation of real-world objects and their corresponding spatial relations in linguistic and physical respects. Physical spatial relations and objects are measured by the visual system, whereas linguistic spatial relations and objects are required for interactions with a user.

  16. Secure wavelet-based isometric projection for face recognition

    NASA Astrophysics Data System (ADS)

    Al-Assam, Hisham; Sellahewa, Harin; Jassim, Sabah A.

    2011-06-01

    Biometric systems such as face recognition must address four key challenges: efficiency, robustness, accuracy and security. Isometric projection has been proposed as a robust dimension reduction technique for a number of applications, but it is computationally demanding when applied to high dimensional spaces such as the space of face images. On the other hand, wavelet transforms have shown to provide an efficient tool for facial feature representation and face recognition with significant reduction in dimension. In this paper, we propose a hybrid approach that combines the efficiency and robustness of wavelet transforms with isometric projections for face features extraction in the transformed domain to be used for recognition. We shall compare the recognition accuracy of our approach with the accuracy of other commonly used projection techniques in the wavelet domain such as PCA and LDA. The security of biometric templates is addressed by adopting a lightweight random projection technique as an add-on subsystem. The results are based on experiments conducted on a publicly available benchmark face database.

  17. Coupled kernel embedding for low resolution face image recognition.

    PubMed

    Ren, Chuan-Xian; Dai, Dao-Qing; Yan, Hong

    2012-08-01

    Practical video scene and face recognition systems are sometimes confronted with low-resolution (LR) images. The faces may be very small even if the video is clear, thus it is difficult to directly measure the similarity between the faces and the high-resolution (HR) training samples. Traditional super-resolution (SR) methods based face recognition usually have limited performance because the target of SR may not be consistent with that of classification, and time-consuming SR algorithms are not suitable for real-time applications. In this paper, a new feature extraction method called Coupled Kernel Embedding (CKE) is proposed for LR face recognition without any SR preprocessing. In this method, the final kernel matrix is constructed by concatenating two individual kernel matrices in the diagonal direction, and the (semi-)positively definite properties are preserved for optimization. CKE addresses the problem of comparing multi-modal data that are difficult for conventional methods in practice due to the lack of an efficient similarity measure. Particularly, different kernel types (e.g., linear, Gaussian, polynomial) can be integrated into an uniformed optimization objective, which cannot be achieved by simple linear methods. CKE solves this problem by minimizing the dissimilarities captured by their kernel Gram matrices in the low- and high-resolution spaces. In the implementation, the nonlinear objective function is minimized by a generalized eigenvalue decomposition. Experiments on benchmark and real databases show that our CKE method indeed improves the recognition performance.

  18. Two dimensional discriminant neighborhood preserving embedding in face recognition

    NASA Astrophysics Data System (ADS)

    Pang, Meng; Jiang, Jifeng; Lin, Chuang; Wang, Binghui

    2015-03-01

    One of the key issues of face recognition is to extract the features of face images. In this paper, we propose a novel method, named two-dimensional discriminant neighborhood preserving embedding (2DDNPE), for image feature extraction and face recognition. 2DDNPE benefits from four techniques, i.e., neighborhood preserving embedding (NPE), locality preserving projection (LPP), image based projection and Fisher criterion. Firstly, NPE and LPP are two popular manifold learning techniques which can optimally preserve the local geometry structures of the original samples from different angles. Secondly, image based projection enables us to directly extract the optimal projection vectors from twodimensional image matrices rather than vectors, which avoids the small sample size problem as well as reserves useful structural information embedded in the original images. Finally, the Fisher criterion applied in 2DDNPE can boost face recognition rates by minimizing the within-class distance, while maximizing the between-class distance. To evaluate the performance of 2DDNPE, several experiments are conducted on the ORL and Yale face datasets. The results corroborate that 2DDNPE outperforms the existing 1D feature extraction methods, such as NPE, LPP, LDA and PCA across all experiments with respect to recognition rate and training time. 2DDNPE also delivers consistently promising results compared with other competing 2D methods such as 2DNPP, 2DLPP, 2DLDA and 2DPCA.

  19. Efficient Detection of Occlusion prior to Robust Face Recognition

    PubMed Central

    Dugelay, Jean-Luc

    2014-01-01

    While there has been an enormous amount of research on face recognition under pose/illumination/expression changes and image degradations, problems caused by occlusions attracted relatively less attention. Facial occlusions, due, for example, to sunglasses, hat/cap, scarf, and beard, can significantly deteriorate performances of face recognition systems in uncontrolled environments such as video surveillance. The goal of this paper is to explore face recognition in the presence of partial occlusions, with emphasis on real-world scenarios (e.g., sunglasses and scarf). In this paper, we propose an efficient approach which consists of first analysing the presence of potential occlusion on a face and then conducting face recognition on the nonoccluded facial regions based on selective local Gabor binary patterns. Experiments demonstrate that the proposed method outperforms the state-of-the-art works including KLD-LGBPHS, S-LNMF, OA-LBP, and RSC. Furthermore, performances of the proposed approach are evaluated under illumination and extreme facial expression changes provide also significant results. PMID:24526902

  20. Fusiform gyrus face-selectivity reflects facial recognition ability

    PubMed Central

    Furl, N.; Garrido, L.; Dolan, R.; Driver, J.; Duchaine, B.

    2012-01-01

    Regions of the occipital and temporal lobes, including a region in the fusiform gyrus (FG), have been proposed to comprise a “core” visual representation system for faces, in part because they show face selectivity and face repetition suppression. But recent fMRI studies of developmental prosopagnosics (DPs) raise questions about whether these measures relate to face processing skills. Although DPs manifest deficient face processing, most studies to date have not shown unequivocal reductions of functional responses in the proposed core regions. We scanned 15 DPs and 15 non-DP control participants with fMRI while employing factor analysis to derive behavioral components related to face identification or other processes. Repetition suppression specific to facial identities in FG or to expression in FG and STS did not show compelling relationships with face identification ability. However, we identified robust relationships between face selectivity and face identification ability in FG across our sample for several convergent measures, including voxel-wise statistical parametric mapping, peak face selectivity in individually defined “fusiform face areas” (FFAs), and anatomical extents (cluster sizes) of those FFAs. None of these measures showed associations with behavioral expression or object recognition ability. As a group, DPs had reduced face-selective responses in bilateral FFA when compared with non-DPs. Individual DPs were also more likely than non-DPs to lack expected face-selective activity in core regions. These findings associate individual differences in face processing ability with selectivity in core face processing regions. This confirms that face selectivity can provide a valid marker for neural mechanisms that contribute to face identification ability. PMID:20617881

  1. The Role of Higher Level Adaptive Coding Mechanisms in the Development of Face Recognition

    ERIC Educational Resources Information Center

    Pimperton, Hannah; Pellicano, Elizabeth; Jeffery, Linda; Rhodes, Gillian

    2009-01-01

    DevDevelopmental improvements in face identity recognition ability are widely documented, but the source of children's immaturity in face recognition remains unclear. Differences in the way in which children and adults visually represent faces might underlie immaturities in face recognition. Recent evidence of a face identity aftereffect (FIAE),…

  2. Robust Detection of Round Shaped Pits Lying on 3D Meshes: Application to Impact Crater Recognition

    NASA Astrophysics Data System (ADS)

    Schmidt, Martin-Pierre; Muscato, Jennifer; Viseur, Sophie; Jorda, Laurent; Bouley, Sylvain; Mari, Jean-Luc

    2015-04-01

    Most celestial bodies display impacts of collisions with asteroids and meteoroids. These traces are called craters. The possibility of observing and identifying these craters and their characteristics (radius, depth and morphology) is the only method available to measure the age of different units at the surface of the body, which in turn allows to constrain its conditions of formation. Interplanetary space probes always carry at least one imaging instrument on board. The visible images of the target are used to reconstruct high-resolution 3D models of its surface as a cloud of points in the case of multi-image dense stereo, or as a triangular mesh in the case of stereo and shape-from-shading. The goal of this work is to develop a methodology to automatically detect the craters lying on these 3D models. The robust extraction of feature areas on surface objects embedded in 3D, like circular pits, is a challenging problem. Classical approaches generally rely on image processing and template matching on a 2D flat projection of the 3D object (i.e.: a high-resolution photograph). In this work, we propose a full-3D method that mainly relies on curvature analysis. Mean and Gaussian curvatures are estimated on the surface. They are used to label vertices that belong to concave parts corresponding to specific pits on the surface. The surface is thus transformed into binary map distinguishing potential crater features to other types of features. Centers are located in the targeted surface regions, corresponding to potential crater features. Concentric rings are then built around the found centers. They consist in circular closed lines exclusively composed of edges of the initial mesh. The first built ring represents the nearest vertex neighborhood of the found center. The ring is then optimally expanded using a circularity constrain and the curvature values of the ring vertices. This method has been tested on a 3D model of the asteroid Lutetia observed by the ROSETTA (ESA

  3. The effect of feature displacement on face recognition.

    PubMed

    Haig, Nigel D

    2013-01-01

    Human beings possess a remarkable ability to recognise familiar faces quickly and without apparent effort. In spite of this facility, the mechanisms of visual recognition remain tantalisingly obscure. An experiment is reported in which image processing equipment was used to displace slightly the features of a set of original facial images to form groups of modified images. Observers were then required to indicate whether they were being shown the "original" or a "modified" face, when shown one face at a time on a TV monitor screen. Memory reinforcement was provided by displaying the original face at another screen position, between presentations. The data show, inter alia, the very high significance of the vertical positioning of the mouth, followed by eyes, and then the nose, as well as high sensitivity to close-set eyes, coupled with marked insensitivity to wide-set eyes. Implications of the results for the use of recognition aids such as Identikit and Photofit are briefly discussed.

  4. Face recognition with histograms of fractional differential gradients

    NASA Astrophysics Data System (ADS)

    Yu, Lei; Ma, Yan; Cao, Qi

    2014-05-01

    It has proved that fractional differentiation can enhance the edge information and nonlinearly preserve textural detailed information in an image. This paper investigates its ability for face recognition and presents a local descriptor called histograms of fractional differential gradients (HFDG) to extract facial visual features. HFDG encodes a face image into gradient patterns using multiorientation fractional differential masks, from which histograms of gradient directions are computed as the face representation. Experimental results on Yale, face recognition technology (FERET), Carnegie Mellon University pose, illumination, and expression (CMU PIE), and A. Martinez and R. Benavente (AR) databases validate the feasibility of the proposed method and show that HFDG outperforms local binary patterns (LBP), histograms of oriented gradients (HOG), enhanced local directional patterns (ELDP), and Gabor feature-based methods.

  5. A Study on Representations for Face Recognition from Thermal Images

    NASA Astrophysics Data System (ADS)

    Plasencia, Yenisel; García-Reyes, Edel; Duin, Robert P. W.; Mendez-Vazquez, Heydi; San-Martin, César; Soto, Claudio

    Two challenges of face recognition at a distance are the uncontrolled illumination and the low resolution of the images. One approach to tackle the first limitation is to use longwave infrared face images since they are invariant to illumination changes. In this paper we study classification performances on 3 different representations: pixel-based, histogram, and dissimilarity representation based on histogram distances for face recognition from low resolution longwave infrared images. The experiments show that the optimal representation depends on the resolution of images and histogram bins. It was also observed that low resolution thermal images joined to a proper representation are sufficient to discriminate between subjects and we suggest that they can be promising for applications such as face tracking.

  6. Semisupervised kernel marginal Fisher analysis for face recognition.

    PubMed

    Wang, Ziqiang; Sun, Xia; Sun, Lijun; Huang, Yuchun

    2013-01-01

    Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm.

  7. Semisupervised Kernel Marginal Fisher Analysis for Face Recognition

    PubMed Central

    Wang, Ziqiang; Sun, Xia; Sun, Lijun; Huang, Yuchun

    2013-01-01

    Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm. PMID:24163638

  8. Why the long face? The importance of vertical image structure for biological "barcodes" underlying face recognition.

    PubMed

    Spence, Morgan L; Storrs, Katherine R; Arnold, Derek H

    2014-07-29

    Humans are experts at face recognition. The mechanisms underlying this complex capacity are not fully understood. Recently, it has been proposed that face recognition is supported by a coarse-scale analysis of visual information contained in horizontal bands of contrast distributed along the vertical image axis-a biological facial "barcode" (Dakin & Watt, 2009). A critical prediction of the facial barcode hypothesis is that the distribution of image contrast along the vertical axis will be more important for face recognition than image distributions along the horizontal axis. Using a novel paradigm involving dynamic image distortions, a series of experiments are presented examining famous face recognition impairments from selectively disrupting image distributions along the vertical or horizontal image axes. Results show that disrupting the image distribution along the vertical image axis is more disruptive for recognition than matched distortions along the horizontal axis. Consistent with the facial barcode hypothesis, these results suggest that human face recognition relies disproportionately on appropriately scaled distributions of image contrast along the vertical image axis. © 2014 ARVO.

  9. Possible use of small UAV to create high resolution 3D model of vertical rock faces

    NASA Astrophysics Data System (ADS)

    Mészáros, János; Kerkovits, Krisztian

    2014-05-01

    One of the newest and mostly emerging acquisition technologies is the use of small unmanned aerial vehicles (UAVs) to photogrammetry and remote sensing. Several successful research project or industrial use can be found worldwide (mine investigation, precision agriculture, mapping etc.) but those surveys are focusing mainly on the survey of horizontal areas. In our research a mixed acquisition method was developed and tested to create a dense, 3D model about a columnar outcrop close to Kő-hegy (Pest County). Our primary goal was to create a model whereat the pattern of different layers is clearly visible and measurable, as well as to test the robustness of our idea. Our method uses a consumer grade camera to take digital photographs about the outcrop. A small, custom made tricopter was built to carry the camera above middle and top parts of the rock, the bottom part can be photographed only from several ground positions. During the field survey ground control points were installed and measured using a kinematic correction GPS. These latter data were used during the georeferencing of generated point cloud. Free online services built on Structure from Motion (SfM) algorithms and desktop software also were tested to generate the relative point cloud and for further processing and analysis.

  10. Quantitative analysis and feature recognition in 3-D microstructural data sets

    NASA Astrophysics Data System (ADS)

    Lewis, A. C.; Suh, C.; Stukowski, M.; Geltmacher, A. B.; Spanos, G.; Rajan, K.

    2006-12-01

    A three-dimensional (3-D) reconstruction of an austenitic stainless-steel microstructure was used as input for an image-based finite-element model to simulate the anisotropic elastic mechanical response of the microstructure. The quantitative data-mining and data-warehousing techniques used to correlate regions of high stress with critical microstructural features are discussed. Initial analysis of elastic stresses near grain boundaries due to mechanical loading revealed low overall correlation with their location in the microstructure. However, the use of data-mining and feature-tracking techniques to identify high-stress outliers revealed that many of these high-stress points are generated near grain boundaries and grain edges (triple junctions). These techniques also allowed for the differentiation between high stresses due to boundary conditions of the finite volume reconstructed, and those due to 3-D microstructural features.

  11. Design of embedded intelligent monitoring system based on face recognition

    NASA Astrophysics Data System (ADS)

    Liang, Weidong; Ding, Yan; Zhao, Liangjin; Li, Jia; Hu, Xuemei

    2017-01-01

    In this paper, a new embedded intelligent monitoring system based on face recognition is proposed. The system uses Pi Raspberry as the central processor. A sensors group has been designed with Zigbee module in order to assist the system to work better and the two alarm modes have been proposed using the Internet and 3G modem. The experimental results show that the system can work under various light intensities to recognize human face and send alarm information in real time.

  12. Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes.

    PubMed

    Yebes, J Javier; Bergasa, Luis M; García-Garrido, Miguel Ángel

    2015-04-20

    Driver assistance systems and autonomous robotics rely on the deployment of several sensors for environment perception. Compared to LiDAR systems, the inexpensive vision sensors can capture the 3D scene as perceived by a driver in terms of appearance and depth cues. Indeed, providing 3D image understanding capabilities to vehicles is an essential target in order to infer scene semantics in urban environments. One of the challenges that arises from the navigation task in naturalistic urban scenarios is the detection of road participants (e.g., cyclists, pedestrians and vehicles). In this regard, this paper tackles the detection and orientation estimation of cars, pedestrians and cyclists, employing the challenging and naturalistic KITTI images. This work proposes 3D-aware features computed from stereo color images in order to capture the appearance and depth peculiarities of the objects in road scenes. The successful part-based object detector, known as DPM, is extended to learn richer models from the 2.5D data (color and disparity), while also carrying out a detailed analysis of the training pipeline. A large set of experiments evaluate the proposals, and the best performing approach is ranked on the KITTI website. Indeed, this is the first work that reports results with stereo data for the KITTI object challenge, achieving increased detection ratios for the classes car and cyclist compared to a baseline DPM.

  13. Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes

    PubMed Central

    Yebes, J. Javier; Bergasa, Luis M.; García-Garrido, Miguel Ángel

    2015-01-01

    Driver assistance systems and autonomous robotics rely on the deployment of several sensors for environment perception. Compared to LiDAR systems, the inexpensive vision sensors can capture the 3D scene as perceived by a driver in terms of appearance and depth cues. Indeed, providing 3D image understanding capabilities to vehicles is an essential target in order to infer scene semantics in urban environments. One of the challenges that arises from the navigation task in naturalistic urban scenarios is the detection of road participants (e.g., cyclists, pedestrians and vehicles). In this regard, this paper tackles the detection and orientation estimation of cars, pedestrians and cyclists, employing the challenging and naturalistic KITTI images. This work proposes 3D-aware features computed from stereo color images in order to capture the appearance and depth peculiarities of the objects in road scenes. The successful part-based object detector, known as DPM, is extended to learn richer models from the 2.5D data (color and disparity), while also carrying out a detailed analysis of the training pipeline. A large set of experiments evaluate the proposals, and the best performing approach is ranked on the KITTI website. Indeed, this is the first work that reports results with stereo data for the KITTI object challenge, achieving increased detection ratios for the classes car and cyclist compared to a baseline DPM. PMID:25903553

  14. A new approach for semi-automatic rock mass joints recognition from 3D point clouds

    NASA Astrophysics Data System (ADS)

    Riquelme, Adrián J.; Abellán, A.; Tomás, R.; Jaboyedoff, M.

    2014-07-01

    Rock mass characterization requires a deep geometric understanding of the discontinuity sets affecting rock exposures. Recent advances in Light Detection and Ranging (LiDAR) instrumentation currently allow quick and accurate 3D data acquisition, yielding on the development of new methodologies for the automatic characterization of rock mass discontinuities. This paper presents a methodology for the identification and analysis of flat surfaces outcropping in a rocky slope using the 3D data obtained with LiDAR. This method identifies and defines the algebraic equations of the different planes of the rock slope surface by applying an analysis based on a neighbouring points coplanarity test, finding principal orientations by Kernel Density Estimation and identifying clusters by the Density-Based Scan Algorithm with Noise. Different sources of information - synthetic and 3D scanned data - were employed, performing a complete sensitivity analysis of the parameters in order to identify the optimal value of the variables of the proposed method. In addition, raw source files and obtained results are freely provided in order to allow to a more straightforward method comparison aiming to a more reproducible research.

  15. Characteristics of eye movements in 3-D object learning: comparison between within-modal and cross-modal object recognition.

    PubMed

    Ueda, Yoshiyuki; Saiki, Jun

    2012-01-01

    Recent studies have indicated that the object representation acquired during visual learning depends on the encoding modality during the test phase. However, the nature of the differences between within-modal learning (eg visual learning-visual recognition) and cross-modal learning (eg visual learning-haptic recognition) remains unknown. To address this issue, we utilised eye movement data and investigated object learning strategies during the learning phase of a cross-modal object recognition experiment. Observers informed of the test modality studied an unfamiliar visually presented 3-D object. Quantitative analyses showed that recognition performance was consistent regardless of rotation in the cross-modal condition, but was reduced when objects were rotated in the within-modal condition. In addition, eye movements during learning significantly differed between within-modal and cross-modal learning. Fixations were more diffused for cross-modal learning than in within-modal learning. Moreover, over the course of the trial, fixation durations became longer in cross-modal learning than in within-modal learning. These results suggest that the object learning strategies employed during the learning phase differ according to the modality of the test phase, and that this difference leads to different recognition performances.

  16. Recognition advantage of happy faces: tracing the neurocognitive processes.

    PubMed

    Calvo, Manuel G; Beltrán, David

    2013-09-01

    The present study aimed to identify the brain processes-and their time course-underlying the typical behavioral recognition advantage of happy facial expressions. To this end, we recorded EEG activity during an expression categorization task for happy, angry, fearful, sad, and neutral faces, and the correlation between event-related-potential (ERP) patterns and recognition performance was assessed. N170 (150-180 ms) was enhanced for angry, fearful and sad faces; N2 was reduced and early posterior negativity (EPN; both, 200-320 ms) was enhanced for happy and angry faces; P3b (350-450 ms) was reduced for happy and neutral faces; and slow positive wave (SPW; 700-800 ms) was reduced for happy faces. This reveals (a) an early processing (N170) of negative affective valence (i.e., angry, fearful, and sad), (b) discrimination (N2 and EPN) of affective intensity or arousal (i.e., angry and happy), and (c) facilitated categorization (P3b) and decision (SPW) due to expressive distinctiveness (i.e., happy). In addition, N2, EPN, P3b, and SPW were related to categorization accuracy and speed. This suggests that conscious expression recognition and the typical happy face advantage depend on encoding of expressive intensity and, especially, on later response selection, rather than on the early processing of affective valence. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. Emotion-attention interactions in recognition memory for distractor faces.

    PubMed

    Srinivasan, Narayanan; Gupta, Rashmi

    2010-04-01

    Effective filtering of distractor information has been shown to be dependent on perceptual load. Given the salience of emotional information and the presence of emotion-attention interactions, we wanted to explore the recognition memory for emotional distractors especially as a function of focused attention and distributed attention by manipulating load and the spatial spread of attention. We performed two experiments to study emotion-attention interactions by measuring recognition memory performance for distractor neutral and emotional faces. Participants performed a color discrimination task (low-load) or letter identification task (high-load) with a letter string display in Experiment 1 and a high-load letter identification task with letters presented in a circular array in Experiment 2. The stimuli were presented against a distractor face background. The recognition memory results show that happy faces were recognized better than sad faces under conditions of less focused or distributed attention. When attention is more spatially focused, sad faces were recognized better than happy faces. The study provides evidence for emotion-attention interactions in which specific emotional information like sad or happy is associated with focused or distributed attention respectively. Distractor processing with emotional information also has implications for theories of attention. Copyright 2010 APA, all rights reserved.

  18. Part-based set matching for face recognition in surveillance

    NASA Astrophysics Data System (ADS)

    Zheng, Fei; Wang, Guijin; Lin, Xinggang

    2013-12-01

    Face recognition in surveillance is a hot topic in computer vision due to the strong demand for public security and remains a challenging task owing to large variations in viewpoint and illumination of cameras. In surveillance, image sets are the most natural form of input by incorporating tracking. Recent advances in set-based matching also show its great potential for exploring the feature space for face recognition by making use of multiple samples of subjects. In this paper, we propose a novel method that exploits the salient features (such as eyes, noses, mouth) in set-based matching. To represent image sets, we adopt the affine hull model, which can general unseen appearances in the form of affine combinations of sample images. In our proposal, a robust part detector is first used to find four salient parts for each face image: two eyes, nose, and mouth. For each part, we construct an affine hull model by using the local binary pattern histograms of multiple samples of the part. We also construct an affine model for the whole face region. Then, we find the closest distance between the corresponding affine hull models to measure the similarity between parts/face regions, and a weighting scheme is introduced to combine the five distances (four parts and the whole face region) to obtain the final distance between two subjects. In the recognition phase, a nearest neighbor classifier is used. Experiments on the public ChokePoint dataset and our dataset demonstrate the superior performance of our method.

  19. Holistic face processing can inhibit recognition of forensic facial composites.

    PubMed

    McIntyre, Alex H; Hancock, Peter J B; Frowd, Charlie D; Langton, Stephen R H

    2016-04-01

    Facial composite systems help eyewitnesses to show the appearance of criminals. However, likenesses created by unfamiliar witnesses will not be completely accurate, and people familiar with the target can find them difficult to identify. Faces are processed holistically; we explore whether this impairs identification of inaccurate composite images and whether recognition can be improved. In Experiment 1 (n = 64) an imaging technique was used to make composites of celebrity faces more accurate and identification was contrasted with the original composite images. Corrected composites were better recognized, confirming that errors in production of the likenesses impair identification. The influence of holistic face processing was explored by misaligning the top and bottom parts of the composites (cf. Young, Hellawell, & Hay, 1987). Misalignment impaired recognition of corrected composites but identification of the original, inaccurate composites significantly improved. This effect was replicated with facial composites of noncelebrities in Experiment 2 (n = 57). We conclude that, like real faces, facial composites are processed holistically: recognition is impaired because unlike real faces, composites contain inaccuracies and holistic face processing makes it difficult to perceive identifiable features. This effect was consistent across composites of celebrities and composites of people who are personally familiar. Our findings suggest that identification of forensic facial composites can be enhanced by presenting composites in a misaligned format. (c) 2016 APA, all rights reserved).

  20. Neural and genetic foundations of face recognition and prosopagnosia.

    PubMed

    Grüter, Thomas; Grüter, Martina; Carbon, Claus-Christian

    2008-03-01

    Faces are of essential importance for human social life. They provide valuable information about the identity, expression, gaze, health, and age of a person. Recent face-processing models assume highly interconnected neural structures between different temporal, occipital, and frontal brain areas with several feedback loops. A selective deficit in the visual learning and recognition of faces is known as prosopagnosia, which can be found both in acquired and congenital form. Recently, a hereditary sub-type of congenital prosopagnosia with a very high prevalence rate of 2.5% has been identified. Recent research results show that hereditary prosopagnosia is a clearly circumscribed face-processing deficit with a characteristic set of clinical symptoms. Comparing face processing of people of prosopagnosia with that of controls can help to develop a more conclusive and integrated model of face processing. Here, we provide a summary of the current state of face processing research. We also describe the different types of prosopagnosia and present the set of typical symptoms found in the hereditary type. Finally, we will discuss the implications for future face recognition research.

  1. Orienting to face expression during encoding improves men's recognition of own gender faces.

    PubMed

    Fulton, Erika K; Bulluck, Megan; Hertzog, Christopher

    2015-10-01

    It is unclear why women have superior episodic memory of faces, but the benefit may be partially the result of women engaging in superior processing of facial expressions. Therefore, we hypothesized that orienting instructions to attend to facial expression at encoding would significantly improve men's memory of faces and possibly reduce gender differences. We directed 203 college students (122 women) to study 120 faces under instructions to orient to either the person's gender or their emotional expression. They later took a recognition test of these faces by either judging whether they had previously studied the same person or that person with the exact same expression; the latter test evaluated recollection of specific facial details. Orienting to facial expressions during encoding significantly improved men's recognition of own-gender faces and eliminated the advantage that women had for male faces under gender orienting instructions. Although gender differences in spontaneous strategy use when orienting to faces cannot fully account for gender differences in face recognition, orienting men to facial expression during encoding is one way to significantly improve their episodic memory for male faces. Copyright © 2015 Elsevier B.V. All rights reserved.

  2. Spherical blurred shape model for 3-D object and pose recognition: quantitative analysis and HCI applications in smart environments.

    PubMed

    Lopes, Oscar; Reyes, Miguel; Escalera, Sergio; Gonzàlez, Jordi

    2014-12-01

    The use of depth maps is of increasing interest after the advent of cheap multisensor devices based on structured light, such as Kinect. In this context, there is a strong need of powerful 3-D shape descriptors able to generate rich object representations. Although several 3-D descriptors have been already proposed in the literature, the research of discriminative and computationally efficient descriptors is still an open issue. In this paper, we propose a novel point cloud descriptor called spherical blurred shape model (SBSM) that successfully encodes the structure density and local variabilities of an object based on shape voxel distances and a neighborhood propagation strategy. The proposed SBSM is proven to be rotation and scale invariant, robust to noise and occlusions, highly discriminative for multiple categories of complex objects like the human hand, and computationally efficient since the SBSM complexity is linear to the number of object voxels. Experimental evaluation in public depth multiclass object data, 3-D facial expressions data, and a novel hand poses data sets show significant performance improvements in relation to state-of-the-art approaches. Moreover, the effectiveness of the proposal is also proved for object spotting in 3-D scenes and for real-time automatic hand pose recognition in human computer interaction scenarios.

  3. Famous faces as icons. The illusion of being an expert in the recognition of famous faces.

    PubMed

    Carbon, Claus-Christian

    2008-01-01

    It is a common belief that we are experts in the processing of famous faces. Although our ability to quickly and accurately recognise pictures of famous faces is quite impressive, we might not really process famous faces as faces per se, but as 'icons' or famous still pictures of famous faces. This assumption was tested in two parallel experiments employing a recognition task on famous, but personally unfamiliar, and on personally familiar faces. Both tests included (a) original, 'iconic' pictures, (b) slightly modified versions of familiar pictures, and (c) rather unfamiliar pictures of familiar persons. Participants (n = 70 + 70) indeed recognised original pictures of famous and personally familiar people very accurately, while performing poorly in recognising slightly modified, as well as unfamiliar versions of famous, but not personally familiar persons. These results indicate that the successful processing of famous faces may depend on icons imbued in society but not on the face as such.

  4. From face processing to face recognition: Comparing three different processing levels.

    PubMed

    Besson, G; Barragan-Jason, G; Thorpe, S J; Fabre-Thorpe, M; Puma, S; Ceccaldi, M; Barbeau, E J

    2017-01-01

    Verifying that a face is from a target person (e.g. finding someone in the crowd) is a critical ability of the human face processing system. Yet how fast this can be performed is unknown. The 'entry-level shift due to expertise' hypothesis suggests that - since humans are face experts - processing faces should be as fast - or even faster - at the individual than at superordinate levels. In contrast, the 'superordinate advantage' hypothesis suggests that faces are processed from coarse to fine, so that the opposite pattern should be observed. To clarify this debate, three different face processing levels were compared: (1) a superordinate face categorization level (i.e. detecting human faces among animal faces), (2) a face familiarity level (i.e. recognizing famous faces among unfamiliar ones) and (3) verifying that a face is from a target person, our condition of interest. The minimal speed at which faces can be categorized (∼260ms) or recognized as familiar (∼360ms) has largely been documented in previous studies, and thus provides boundaries to compare our condition of interest to. Twenty-seven participants were included. The recent Speed and Accuracy Boosting procedure paradigm (SAB) was used since it constrains participants to use their fastest strategy. Stimuli were presented either upright or inverted. Results revealed that verifying that a face is from a target person (minimal RT at ∼260ms) was remarkably fast but longer than the face categorization level (∼240ms) and was more sensitive to face inversion. In contrast, it was much faster than recognizing a face as familiar (∼380ms), a level severely affected by face inversion. Face recognition corresponding to finding a specific person in a crowd thus appears achievable in only a quarter of a second. In favor of the 'superordinate advantage' hypothesis or coarse-to-fine account of the face visual hierarchy, these results suggest a graded engagement of the face processing system across processing

  5. Face recognition with multi-resolution spectral feature images.

    PubMed

    Sun, Zhan-Li; Lam, Kin-Man; Dong, Zhao-Yang; Wang, Han; Gao, Qing-Wei; Zheng, Chun-Hou

    2013-01-01

    The one-sample-per-person problem has become an active research topic for face recognition in recent years because of its challenges and significance for real-world applications. However, achieving relatively higher recognition accuracy is still a difficult problem due to, usually, too few training samples being available and variations of illumination and expression. To alleviate the negative effects caused by these unfavorable factors, in this paper we propose a more accurate spectral feature image-based 2DLDA (two-dimensional linear discriminant analysis) ensemble algorithm for face recognition, with one sample image per person. In our algorithm, multi-resolution spectral feature images are constructed to represent the face images; this can greatly enlarge the training set. The proposed method is inspired by our finding that, among these spectral feature images, features extracted from some orientations and scales using 2DLDA are not sensitive to variations of illumination and expression. In order to maintain the positive characteristics of these filters and to make correct category assignments, the strategy of classifier committee learning (CCL) is designed to combine the results obtained from different spectral feature images. Using the above strategies, the negative effects caused by those unfavorable factors can be alleviated efficiently in face recognition. Experimental results on the standard databases demonstrate the feasibility and efficiency of the proposed method.

  6. Simulationist Models of Face-Based Emotion Recognition

    ERIC Educational Resources Information Center

    Goldman, Alvin I.; Sripada, Chandra Sekhar

    2005-01-01

    Recent studies of emotion mindreading reveal that for three emotions, fear, disgust, and anger, deficits in face-based recognition are paired with deficits in the production of the same emotion. What type of mindreading process would explain this pattern of paired deficits? The simulation approach and the theorizing approach are examined to…

  7. Impact of Intention on the ERP Correlates of Face Recognition

    ERIC Educational Resources Information Center

    Guillaume, Fabrice; Tiberghien, Guy

    2013-01-01

    The present study investigated the impact of study-test similarity on face recognition by manipulating, in the same experiment, the expression change (same vs. different) and the task-processing context (inclusion vs. exclusion instructions) as within-subject variables. Consistent with the dual-process framework, the present results showed that…

  8. An Inner Face Advantage in Children's Recognition of Familiar Peers

    ERIC Educational Resources Information Center

    Ge, Liezhong; Anzures, Gizelle; Wang, Zhe; Kelly, David J.; Pascalis, Olivier; Quinn, Paul C.; Slater, Alan M.; Yang, Zhiliang; Lee, Kang

    2008-01-01

    Children's recognition of familiar own-age peers was investigated. Chinese children (4-, 8-, and 14-year-olds) were asked to identify their classmates from photographs showing the entire face, the internal facial features only, the external facial features only, or the eyes, nose, or mouth only. Participants from all age groups were familiar with…

  9. Impact of Intention on the ERP Correlates of Face Recognition

    ERIC Educational Resources Information Center

    Guillaume, Fabrice; Tiberghien, Guy

    2013-01-01

    The present study investigated the impact of study-test similarity on face recognition by manipulating, in the same experiment, the expression change (same vs. different) and the task-processing context (inclusion vs. exclusion instructions) as within-subject variables. Consistent with the dual-process framework, the present results showed that…

  10. Evolutionary-Rough Feature Selection for Face Recognition

    NASA Astrophysics Data System (ADS)

    Mazumdar, Debasis; Mitra, Soma; Mitra, Sushmita

    Elastic Bunch Graph Matching is a feature-based face recognition algorithm which has been used to determine facial attributes from an image. However the dimension of the feature vectors, in case of EBGM, is quite high. Feature selection is a useful preprocessing step for reducing dimensionality, removing irrelevant data, improving learning accuracy and enhancing output comprehensibility.

  11. Emotional Recognition in Autism Spectrum Conditions from Voices and Faces

    ERIC Educational Resources Information Center

    Stewart, Mary E.; McAdam, Clair; Ota, Mitsuhiko; Peppe, Sue; Cleland, Joanne

    2013-01-01

    The present study reports on a new vocal emotion recognition task and assesses whether people with autism spectrum conditions (ASC) perform differently from typically developed individuals on tests of emotional identification from both the face and the voice. The new test of vocal emotion contained trials in which the vocal emotion of the sentence…

  12. Emotional Recognition in Autism Spectrum Conditions from Voices and Faces

    ERIC Educational Resources Information Center

    Stewart, Mary E.; McAdam, Clair; Ota, Mitsuhiko; Peppe, Sue; Cleland, Joanne

    2013-01-01

    The present study reports on a new vocal emotion recognition task and assesses whether people with autism spectrum conditions (ASC) perform differently from typically developed individuals on tests of emotional identification from both the face and the voice. The new test of vocal emotion contained trials in which the vocal emotion of the sentence…

  13. Face Recognition with Multi-Resolution Spectral Feature Images

    PubMed Central

    Sun, Zhan-Li; Lam, Kin-Man; Dong, Zhao-Yang; Wang, Han; Gao, Qing-Wei; Zheng, Chun-Hou

    2013-01-01

    The one-sample-per-person problem has become an active research topic for face recognition in recent years because of its challenges and significance for real-world applications. However, achieving relatively higher recognition accuracy is still a difficult problem due to, usually, too few training samples being available and variations of illumination and expression. To alleviate the negative effects caused by these unfavorable factors, in this paper we propose a more accurate spectral feature image-based 2DLDA (two-dimensional linear discriminant analysis) ensemble algorithm for face recognition, with one sample image per person. In our algorithm, multi-resolution spectral feature images are constructed to represent the face images; this can greatly enlarge the training set. The proposed method is inspired by our finding that, among these spectral feature images, features extracted from some orientations and scales using 2DLDA are not sensitive to variations of illumination and expression. In order to maintain the positive characteristics of these filters and to make correct category assignments, the strategy of classifier committee learning (CCL) is designed to combine the results obtained from different spectral feature images. Using the above strategies, the negative effects caused by those unfavorable factors can be alleviated efficiently in face recognition. Experimental results on the standard databases demonstrate the feasibility and efficiency of the proposed method. PMID:23418451

  14. An Inner Face Advantage in Children's Recognition of Familiar Peers

    ERIC Educational Resources Information Center

    Ge, Liezhong; Anzures, Gizelle; Wang, Zhe; Kelly, David J.; Pascalis, Olivier; Quinn, Paul C.; Slater, Alan M.; Yang, Zhiliang; Lee, Kang

    2008-01-01

    Children's recognition of familiar own-age peers was investigated. Chinese children (4-, 8-, and 14-year-olds) were asked to identify their classmates from photographs showing the entire face, the internal facial features only, the external facial features only, or the eyes, nose, or mouth only. Participants from all age groups were familiar with…

  15. The 3D Elevation Program—Landslide recognition, hazard assessment, and mitigation support

    USGS Publications Warehouse

    Lukas, Vicki; Carswell, Jr., William J.

    2017-01-27

    The U.S. Geological Survey (USGS) Landslide Hazards Program conducts landslide hazard assessments, pursues landslide investigations and forecasts, provides technical assistance to respond to landslide emergencies, and engages in outreach. All of these activities benefit from the availability of high-resolution, three-dimensional (3D) elevation information in the form of light detection and ranging (lidar) data and interferometric synthetic aperture radar (IfSAR) data. Research on landslide processes addresses critical questions of where and when landslides are likely to occur as well as their size, speed, and effects. This understanding informs the development of methods and tools for hazard assessment and situational awareness used to guide efforts to avoid or mitigate landslide impacts. Such research is essential for the USGS to provide improved information on landslide potential associated with severe storms, earthquakes, volcanic activity, coastal wave erosion, and wildfire burn areas.Decisionmakers in government and the private sector increasingly depend on information the USGS provides before, during, and following disasters so that communities can live, work, travel, and build safely. The USGS 3D Elevation Program (3DEP) provides the programmatic infrastructure to generate and supply lidar-derived superior terrain data to address landslide applications and a wide range of other urgent needs nationwide. By providing data to users, 3DEP reduces users’ costs and risks and allows them to concentrate on their mission objectives. 3DEP includes (1) data acquisition partnerships that leverage funding, (2) contracts with experienced private mapping firms, (3) technical expertise, lidar data standards, and specifications, and (4) most important, public access to high-quality 3D elevation data.

  16. Automatic 2D-to-3D video conversion by monocular depth cues fusion and utilizing human face landmarks

    NASA Astrophysics Data System (ADS)

    Fard, Mani B.; Bayazit, Ulug

    2013-12-01

    In this paper, we propose a hybrid 2D-to-3D video conversion system to recover the 3D structure of the scene. Depending on the scene characteristics, geometric or height depth information is adopted to form the initial depth map. This depth map is fused with color-based depth cues to construct the nal depth map of the scene background. The depths of the foreground objects are estimated after their classi cation into human and non-human regions. Speci cally, the depth of a non-human foreground object is directly calculated from the depth of the region behind it in the background. To acquire more accurate depth for the regions containing a human, the estimation of the distance between face landmarks is also taken into account. Finally, the computed depth information of the foreground regions is superimposed on the background depth map to generate the complete depth map of the scene which is the main goal in the process of converting 2D video to 3D.

  17. Effect of severe image compression on face recognition algorithms

    NASA Astrophysics Data System (ADS)

    Zhao, Peilong; Dong, Jiwen; Li, Hengjian

    2015-10-01

    In today's information age, people will depend more and more on computers to obtain and make use of information, there is a big gap between the multimedia information after digitization that has large data and the current hardware technology that can provide the computer storage resources and network band width. For example, there is a large amount of image storage and transmission problem. Image compression becomes useful in cases when images need to be transmitted across networks in a less costly way by increasing data volume while reducing transmission time. This paper discusses image compression to effect on face recognition system. For compression purposes, we adopted the JPEG, JPEG2000, JPEG XR coding standard. The face recognition algorithms studied are SIFT. As a form of an extensive research, Experimental results show that it still maintains a high recognition rate under the high compression ratio, and JPEG XR standards is superior to other two kinds in terms of performance and complexity.

  18. A fast 3-D object recognition algorithm for the vision system of a special-purpose dexterous manipulator

    NASA Technical Reports Server (NTRS)

    Hung, Stephen H. Y.

    1989-01-01

    A fast 3-D object recognition algorithm that can be used as a quick-look subsystem to the vision system for the Special-Purpose Dexterous Manipulator (SPDM) is described. Global features that can be easily computed from range data are used to characterize the images of a viewer-centered model of an object. This algorithm will speed up the processing by eliminating the low level processing whenever possible. It may identify the object, reject a set of bad data in the early stage, or create a better environment for a more powerful algorithm to carry the work further.

  19. Face recognition using local gradient binary count pattern

    NASA Astrophysics Data System (ADS)

    Zhao, Xiaochao; Lin, Yaping; Ou, Bo; Yang, Junfeng; Wu, Zhelun

    2015-11-01

    A local feature descriptor, the local gradient binary count pattern (LGBCP), is proposed for face recognition. Unlike some current methods that extract features directly from a face image in the spatial domain, LGBCP encodes the local gradient information of the face's texture in an effective way and provides a more discriminative code than other methods. We compute the gradient information of a face image through convolutions with compass masks. The gradient information is encoded using the local binary count operator. We divide a face into several subregions and extract the distribution of the LGBCP codes from each subregion. Then all the histograms are concatenated into a vector, which is used for face description. For recognition, the chi-square statistic is used to measure the similarity of different feature vectors. Besides directly calculating the similarity of two feature vectors, we provide a weighted matching scheme in which different weights are assigned to different subregions. The nearest-neighborhood classifier is exploited for classification. Experiments are conducted on the FERET, CAS-PEAL, and AR face databases. LGBCP achieves 96.15% on the Fb set of FERET. For CAS-PEAL, LGBCP gets 96.97%, 98.91%, and 90.89% on the aging, distance, and expression sets, respectively.

  20. The Facespan-the perceptual span for face recognition.

    PubMed

    Papinutto, Michael; Lao, Junpeng; Ramon, Meike; Caldara, Roberto; Miellet, Sébastien

    2017-05-01

    In reading, the perceptual span is a well-established concept that refers to the amount of information that can be read in a single fixation. Surprisingly, despite extensive empirical interest in determining the perceptual strategies deployed to process faces and an ongoing debate regarding the factors or mechanism(s) underlying efficient face processing, the perceptual span for faces-the Facespan-remains undetermined. To address this issue, we applied the gaze-contingent Spotlight technique implemented in an old-new face recognition paradigm. This procedure allowed us to parametrically vary the amount of facial information available at a fixated location in order to determine the minimal aperture size at which face recognition performance plateaus. As expected, accuracy increased nonlinearly with spotlight size apertures. Analyses of Structural Similarity comparing the available information during spotlight and natural viewing conditions indicate that the Facespan-the minimum spatial extent of preserved facial information leading to comparable performance as in natural viewing-encompasses 7° of visual angle in our viewing conditions (size of the face stimulus: 15.6°; viewing distance: 70 cm), which represents 45% of the face. The present findings provide a benchmark for future investigations that will address if and how the Facespan is modulated by factors such as cultural, developmental, idiosyncratic, or task-related differences.

  1. Anti Theft Mechanism Through Face recognition Using FPGA

    NASA Astrophysics Data System (ADS)

    Sundari, Y. B. T.; Laxminarayana, G.; Laxmi, G. Vijaya

    2012-11-01

    The use of vehicle is must for everyone. At the same time, protection from theft is also very important. Prevention of vehicle theft can be done remotely by an authorized person. The location of the car can be found by using GPS and GSM controlled by FPGA. In this paper, face recognition is used to identify the persons and comparison is done with the preloaded faces for authorization. The vehicle will start only when the authorized personís face is identified. In the event of theft attempt or unauthorized personís trial to drive the vehicle, an MMS/SMS will be sent to the owner along with the location. Then the authorized person can alert the security personnel for tracking and catching the vehicle. For face recognition, a Principal Component Analysis (PCA) algorithm is developed using MATLAB. The control technique for GPS and GSM is developed using VHDL over SPTRAN 3E FPGA. The MMS sending method is written in VB6.0. The proposed application can be implemented with some modifications in the systems wherever the face recognition or detection is needed like, airports, international borders, banking applications etc.

  2. The face of Glut1-DS patients: A 3D Craniofacial Morphometric Analysis.

    PubMed

    Pucciarelli, Valentina; Bertoli, Simona; Codari, Marina; De Amicis, Ramona; De Giorgis, Valentina; Battezzati, Alberto; Veggiotti, Pierangelo; Sforza, Chiarella

    2017-07-01

    Glut1 deficiency syndrome (Glut1-DS) is a neurological and metabolic disorder caused by impaired transport of glucose across the blood brain barrier (BBB). Mutations on the SCL2A1 gene encoding the glucose transporter protein in the BBB cause the syndrome, which encompasses epilepsy, movement disorders, and mental delay. Such variability of symptoms presents an obstacle to early diagnosis. The patients seem to share some craniofacial features, and identification and quantification of these could help in prompt diagnosis and clinical management. We performed a three-dimensional morphometric analysis of the faces of 11 female Glut1-DS patients using a stereophotogrammetric system. Data were analyzed using both inter-landmark distances and Principal Component Analysis. Compared with data collected from age-, sex-, and ethnicity-matched control subjects, common and homogenous facial features were identified among patients, which were mainly located in the mandible and the eyes. Glut1-DS patients had a more anterior chin; their mandibular body was longer but the rami were shorter, with a reduced gonial angle; they had smaller and down-slanted eyes with a reduced intercanthal distance. This study highlights the importance of morphometric analysis for defining the facial anatomical characteristics of the syndrome better, potentially helping clinicians to diagnose Glut1-DS. Improved knowledge of the facial anatomy of these patients can provide insights into their facial and cerebral embryological development, perhaps further clarifying the molecular basis of the syndrome. Clin. Anat. 30:644-652, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  3. Multi-stream face recognition for crime-fighting

    NASA Astrophysics Data System (ADS)

    Jassim, Sabah A.; Sellahewa, Harin

    2007-04-01

    Automatic face recognition (AFR) is a challenging task that is increasingly becoming the preferred biometric trait for identification and has the potential of becoming an essential tool in the fight against crime and terrorism. Closed-circuit television (CCTV) cameras have increasingly been used over the last few years for surveillance in public places such as airports, train stations and shopping centers. They are used to detect and prevent crime, shoplifting, public disorder and terrorism. The work of law-enforcing and intelligence agencies is becoming more reliant on the use of databases of biometric data for large section of the population. Face is one of the most natural biometric traits that can be used for identification and surveillance. However, variations in lighting conditions, facial expressions, face size and pose are a great obstacle to AFR. This paper is concerned with using waveletbased face recognition schemes in the presence of variations of expressions and illumination. In particular, we will investigate the use of a combination of wavelet frequency channels for a multi-stream face recognition using various wavelet subbands as different face signal streams. The proposed schemes extend our recently developed face veri.cation scheme for implementation on mobile devices. We shall present experimental results on the performance of our proposed schemes for a number of face databases including a new AV database recorded on a PDA. By analyzing the various experimental data, we shall demonstrate that the multi-stream approach is robust against variations in illumination and facial expressions than the previous single-stream approach.

  4. Uncorrelated and discriminative graph embedding for face recognition

    NASA Astrophysics Data System (ADS)

    Peng, Chengyu; Li, Jianwei; Huang, Hong

    2011-07-01

    We present a novel feature extraction algorithm for face recognition called the uncorrelated and discriminative graph embedding (UDGE) algorithm, which incorporates graph embedding and local scaling method and obtains uncorrelated discriminative vectors in the projected subspace. An optimization objective function is herein defined to make the discriminative projections preserve the intrinsic neighborhood geometry of the within-class samples while enlarging the margins of between-class samples near to the class boundaries. UDGE efficiently dispenses with a prespecified parameter which is data-dependent to balance the objective of the within-class locality and the between-class locality in comparison with the linear extension of graph embedding in a face recognition scenario. Moreover, it can address the small sample-size problem, and its classification accuracy is not sensitive to neighbor samples size and weight value, as well. Extensive experiments on extended YaleB, CMU PIE, and Indian face databases demonstrate the effectiveness of UDGE.

  5. Face recognition with the Karhunen-Loeve transform

    NASA Astrophysics Data System (ADS)

    Suarez, Pedro F.

    1991-12-01

    The major goal of this research was to investigate machine recognition of faces. The approach taken to achieve this goal was to investigate the use of Karhunen-Loe've Transform (KLT) by implementing flexible and practical code. The KLT utilizes the eigenvectors of the covariance matrix as a basis set. Faces were projected onto the eigenvectors, called eigenfaces, and the resulting projection coefficients were used as features. Face recognition accuracies for the KLT coefficients were superior to Fourier based techniques. Additionally, this thesis demonstrated the image compression and reconstruction capabilities of the KLT. This theses also developed the use of the KLT as a facial feature detector. The ability to differentiate between facial features provides a computer communications interface for non-vocal people with cerebral palsy. Lastly, this thesis developed a KLT based axis system for laser scanner data of human heads. The scanner data axis system provides the anthropometric community a more precise method of fitting custom helmets.

  6. Face-blind for other-race faces: Individual differences in other-race recognition impairments.

    PubMed

    Wan, Lulu; Crookes, Kate; Dawel, Amy; Pidcock, Madeleine; Hall, Ashleigh; McKone, Elinor

    2017-01-01

    We report the existence of a previously undescribed group of people, namely individuals who are so poor at recognition of other-race faces that they meet criteria for clinical-level impairment (i.e., they are "face-blind" for other-race faces). Testing 550 participants, and using the well-validated Cambridge Face Memory Test for diagnosing face blindness, results show the rate of other-race face blindness to be nontrivial, specifically 8.1% of Caucasians and Asians raised in majority own-race countries. Results also show risk factors for other-race face blindness to include: a lack of interracial contact; and being at the lower end of the normal range of general face recognition ability (i.e., even for own-race faces); but not applying less individuating effort to other-race than own-race faces. Findings provide a potential resolution of contradictory evidence concerning the importance of the other-race effect (ORE), by explaining how it is possible for the mean ORE to be modest in size (suggesting a genuine but minor problem), and simultaneously for individuals to suffer major functional consequences in the real world (e.g., eyewitness misidentification of other-race offenders leading to wrongful imprisonment). Findings imply that, in legal settings, evaluating an eyewitness's chance of having made an other-race misidentification requires information about the underlying face recognition abilities of the individual witness. Additionally, analogy with prosopagnosia (inability to recognize even own-race faces) suggests everyday social interactions with other-race people, such as those between colleagues in the workplace, will be seriously impacted by the ORE in some people. (PsycINFO Database Record

  7. Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration.

    PubMed

    Wang, Panqu; Gauthier, Isabel; Cottrell, Garrison

    2016-04-01

    Are face and object recognition abilities independent? Although it is commonly believed that they are, Gauthier et al. [Gauthier, I., McGugin, R. W., Richler, J. J., Herzmann, G., Speegle, M., & VanGulick, A. E. Experience moderates overlap between object and face recognition, suggesting a common ability. Journal of Vision, 14, 7, 2014] recently showed that these abilities become more correlated as experience with nonface categories increases. They argued that there is a single underlying visual ability, v, that is expressed in performance with both face and nonface categories as experience grows. Using the Cambridge Face Memory Test and the Vanderbilt Expertise Test, they showed that the shared variance between Cambridge Face Memory Test and Vanderbilt Expertise Test performance increases monotonically as experience increases. Here, we address why a shared resource across different visual domains does not lead to competition and to an inverse correlation in abilities? We explain this conundrum using our neurocomputational model of face and object processing ["The Model", TM, Cottrell, G. W., & Hsiao, J. H. Neurocomputational models of face processing. In A. J. Calder, G. Rhodes, M. Johnson, & J. Haxby (Eds.), The Oxford handbook of face perception. Oxford, UK: Oxford University Press, 2011]. We model the domain general ability v as the available computational resources (number of hidden units) in the mapping from input to label and experience as the frequency of individual exemplars in an object category appearing during network training. Our results show that, as in the behavioral data, the correlation between subordinate level face and object recognition accuracy increases as experience grows. We suggest that different domains do not compete for resources because the relevant features are shared between faces and objects. The essential power of experience is to generate a "spreading transform" for faces (separating them in representational space) that

  8. The 3-D image recognition based on fuzzy neural network technology

    NASA Technical Reports Server (NTRS)

    Hirota, Kaoru; Yamauchi, Kenichi; Murakami, Jun; Tanaka, Kei

    1993-01-01

    Three dimensional stereoscopic image recognition system based on fuzzy-neural network technology was developed. The system consists of three parts; preprocessing part, feature extraction part, and matching part. Two CCD color camera image are fed to the preprocessing part, where several operations including RGB-HSV transformation are done. A multi-layer perception is used for the line detection in the feature extraction part. Then fuzzy matching technique is introduced in the matching part. The system is realized on SUN spark station and special image input hardware system. An experimental result on bottle images is also presented.

  9. Spatial location in brief, free-viewing face encoding modulates contextual face recognition

    PubMed Central

    Felisberti, Fatima M.; McDermott, Mark R.

    2013-01-01

    The effect of the spatial location of faces in the visual field during brief, free-viewing encoding in subsequent face recognition is not known. This study addressed this question by tagging three groups of faces with cheating, cooperating or neutral behaviours and presenting them for encoding in two visual hemifields (upper vs. lower or left vs. right). Participants then had to indicate if a centrally presented face had been seen before or not. Head and eye movements were free in all phases. Findings showed that the overall recognition of cooperators was significantly better than cheaters, and it was better for faces encoded in the upper hemifield than in the lower hemifield, both in terms of a higher d′ and faster reaction time (RT). The d′ for any given behaviour in the left and right hemifields was similar. The RT in the left hemifield did not vary with tagged behaviour, whereas the RT in the right hemifield was longer for cheaters than for cooperators. The results showed that memory biases in contextual face recognition were modulated by the spatial location of briefly encoded faces and are discussed in terms of scanning reading habits, top-left bias in lighting preference and peripersonal space. PMID:24349694

  10. A Multi-Modal Face Recognition Method Using Complete Local Derivative Patterns and Depth Maps

    PubMed Central

    Yin, Shouyi; Dai, Xu; Ouyang, Peng; Liu, Leibo; Wei, Shaojun

    2014-01-01

    In this paper, we propose a multi-modal 2D + 3D face recognition method for a smart city application based on a Wireless Sensor Network (WSN) and various kinds of sensors. Depth maps are exploited for the 3D face representation. As for feature extraction, we propose a new feature called Complete Local Derivative Pattern (CLDP). It adopts the idea of layering and has four layers. In the whole system, we apply CLDP separately on Gabor features extracted from a 2D image and depth map. Then, we obtain two features: CLDP-Gabor and CLDP-Depth. The two features weighted by the corresponding coefficients are combined together in the decision level to compute the total classification distance. At last, the probe face is assigned the identity with the smallest classification distance. Extensive experiments are conducted on three different databases. The results demonstrate the robustness and superiority of the new approach. The experimental results also prove that the proposed multi-modal 2D + 3D method is superior to other multi-modal ones and CLDP performs better than other Local Binary Pattern (LBP) based features. PMID:25333290

  11. A multi-modal face recognition method using complete local derivative patterns and depth maps.

    PubMed

    Yin, Shouyi; Dai, Xu; Ouyang, Peng; Liu, Leibo; Wei, Shaojun

    2014-10-20

    In this paper, we propose a multi-modal 2D + 3D face recognition method for a smart city application based on a Wireless Sensor Network (WSN) and various kinds of sensors. Depth maps are exploited for the 3D face representation. As for feature extraction, we propose a new feature called Complete Local Derivative Pattern (CLDP). It adopts the idea of layering and has four layers. In the whole system, we apply CLDP separately on Gabor features extracted from a 2D image and depth map. Then, we obtain two features: CLDP-Gabor and CLDP-Depth. The two features weighted by the corresponding coefficients are combined together in the decision level to compute the total classification distance. At last, the probe face is assigned the identity with the smallest classification distance. Extensive experiments are conducted on three different databases. The results demonstrate the robustness and superiority of the new approach. The experimental results also prove that the proposed multi-modal 2D + 3D method is superior to other multi-modal ones and CLDP performs better than other Local Binary Pattern (LBP) based features.

  12. Genome-Wide Identification and 3D Modeling of Proteins involved in DNA Damage Recognition and Repair (Final Report)

    SciTech Connect

    Abagyan, Ruben; An, Jianghong

    2005-08-12

    DNA Damage Recognition and Repair (DDR&R) proteins play a critical role in cellular responses to low-dose radiation and are associated with cancer. We have performed a systematic, genome-wide computational analysis of genomic data for human genes involved in the DDR&R process. The significant achievements of this project include: 1) Construction of the computational pipeline for searching DDR&R genes, building and validation of 3D models of proteins involved in DDR&R; 2) Functional and structural annotation of the 3D models and generation of comprehensive lists of suggested knock-out mutations; and the development of a method to predict the effects of mutations. Large scale testing of technology to identify novel small binding pockets in protein structures leading to new DDRR inhibitor strategies 3) Improvements of macromolecular docking technology (see the CAPRI 1-3 and 4-5 results) 4) Development of a new algorithm for improved analysis of high-density oligonucleotide arrays for gene expression profiling; 5) Construction and maintenance of the DNA Damage Recognition and Repair Database; 6) Producing 15 research papers (12 published and 3 in preparation).

  13. Genome-Wide Identification and 3D Modeling of Proteins involved in DNA Damage Recognition and Repair (Final Report)

    SciTech Connect

    Ruben A. Abagyan, PhD

    2004-04-15

    OAK-B135 DNA Damage Recognition and Repair (DDR and R) proteins play a critical role in cellular responses to low-dose radiation and are associated with cancer. the authors have performed a systematic, genome-wide computational analysis of genomic data for human genes involved in the DDR and R process. The significant achievements of this project include: (1) Construction of the computational pipeline for searching DDR and R genes, building and validation of 3D models of proteins involved in DDR and R; (2) Functional and structural annotation of the 3D models and generation of comprehensive lists of suggested knock-out mutations; (3) Important improvement of macromolecular docking technology and its application to predict the DNA-Protein complex conformation; (4) Development of a new algorithm for improved analysis of high-density oligonucleotide arrays for gene expression profiling; (5) Construction and maintenance of the DNA Damage Recognition and Repair Database; and (6) Producing 14 research papers (10 published and 4 in preparation).

  14. Neural Mechanism for Mirrored Self-face Recognition.

    PubMed

    Sugiura, Motoaki; Miyauchi, Carlos Makoto; Kotozaki, Yuka; Akimoto, Yoritaka; Nozawa, Takayuki; Yomogida, Yukihito; Hanawa, Sugiko; Yamamoto, Yuki; Sakuma, Atsushi; Nakagawa, Seishu; Kawashima, Ryuta

    2015-09-01

    Self-face recognition in the mirror is considered to involve multiple processes that integrate 2 perceptual cues: temporal contingency of the visual feedback on one's action (contingency cue) and matching with self-face representation in long-term memory (figurative cue). The aim of this study was to examine the neural bases of these processes by manipulating 2 perceptual cues using a "virtual mirror" system. This system allowed online dynamic presentations of real-time and delayed self- or other facial actions. Perception-level processes were identified as responses to only a single perceptual cue. The effect of the contingency cue was identified in the cuneus. The regions sensitive to the figurative cue were subdivided by the response to a static self-face, which was identified in the right temporal, parietal, and frontal regions, but not in the bilateral occipitoparietal regions. Semantic- or integration-level processes, including amodal self-representation and belief validation, which allow modality-independent self-recognition and the resolution of potential conflicts between perceptual cues, respectively, were identified in distinct regions in the right frontal and insular cortices. The results are supportive of the multicomponent notion of self-recognition and suggest a critical role for contingency detection in the co-emergence of self-recognition and empathy in infants. © The Author 2014. Published by Oxford University Press.

  15. A new theoretical approach to improving face recognition in disorders of central vision: face caricaturing.

    PubMed

    Irons, Jessica; McKone, Elinor; Dumbleton, Rachael; Barnes, Nick; He, Xuming; Provis, Jan; Ivanovici, Callin; Kwa, Alisa

    2014-02-17

    Damage to central vision, of which age-related macular degeneration (AMD) is the most common cause, leaves patients with only blurred peripheral vision. Previous approaches to improving face recognition in AMD have employed image manipulations designed to enhance early-stage visual processing (e.g., magnification, increased HSF contrast). Here, we argue that further improvement may be possible by targeting known properties of mid- and/or high-level face processing. We enhance identity-related shape information in the face by caricaturing each individual away from an average face. We simulate early- through late-stage AMD-blur by filtering spatial frequencies to mimic the amount of blurring perceived at approximately 10° through 30° into the periphery (assuming a face seen premagnified on a tablet computer). We report caricature advantages for all blur levels, for face viewpoints from front view to semiprofile, and in tasks involving perceiving differences in facial identity between pairs of people, remembering previously learned faces, and rejecting new faces as unknown. Results provide a proof of concept that caricaturing may assist in improving face recognition in AMD and other disorders of central vision.

  16. A wavelet-based approach to face verification/recognition

    NASA Astrophysics Data System (ADS)

    Jassim, Sabah; Sellahewa, Harin

    2005-10-01

    Face verification/recognition is a tough challenge in comparison to identification based on other biometrics such as iris, or fingerprints. Yet, due to its unobtrusive nature, the face is naturally suitable for security related applications. Face verification process relies on feature extraction from face images. Current schemes are either geometric-based or template-based. In the latter, the face image is statistically analysed to obtain a set of feature vectors that best describe it. Performance of a face verification system is affected by image variations due to illumination, pose, occlusion, expressions and scale. This paper extends our recent work on face verification for constrained platforms, where the feature vector of a face image is the coefficients in the wavelet transformed LL-subbands at depth 3 or more. It was demonstrated that the wavelet-only feature vector scheme has a comparable performance to sophisticated state-of-the-art when tested on two benchmark databases (ORL, and BANCA). The significance of those results stem from the fact that the size of the k-th LL- subband is 1/4k of the original image size. Here, we investigate the use of wavelet coefficients in various subbands at level 3 or 4 using various wavelet filters. We shall compare the performance of the wavelet-based scheme for different filters at different subbands with a number of state-of-the-art face verification/recognition schemes on two benchmark databases, namely ORL and the control section of BANCA. We shall demonstrate that our schemes have comparable performance to (or outperform) the best performing other schemes.

  17. Occluded human recognition for a leader following system using 3D range and image data in forest environment

    NASA Astrophysics Data System (ADS)

    Cho, Kuk; Ilyas, Muhammad; Baeg, Seung-Ho; Park, Sangdeok

    2014-06-01

    This paper describes the occluded target recognition and tracking method for a leader-following system by fusing 3D range and image data acquired from 3D light detection and ranging (LIDAR) and a color camera installed on an autonomous vehicle in forest environment. During 3D data processing, the distance-based clustering method has an instinctive problem in close encounters. In the tracking phase, we divide an object tracking process into three phases based on occlusion scenario; before an occlusion (BO) phase, a partially or fully occlusion phase and after an occlusion (AO) phase. To improve the data association performance, we use camera's rich information to find correspondence among objects during above mentioned three phases of occlusion. In this paper, we solve a correspondence problem using the color features of human objects with the sum of squared distance (SSD) and the normalized cross correlation (NCC). The features are integrated with derived windows from Harris corner. The experimental results for a leader following on an autonomous vehicle are shown with LIDAR and camera for improving a data association problem in a multiple object tracking system.

  18. A Highly Accurate Face Recognition System Using Filtering Correlation

    NASA Astrophysics Data System (ADS)

    Watanabe, Eriko; Ishikawa, Sayuri; Kodate, Kashiko

    2007-09-01

    The authors previously constructed a highly accurate fast face recognition optical correlator (FARCO) [E. Watanabe and K. Kodate: Opt. Rev. 12 (2005) 460], and subsequently developed an improved, super high-speed FARCO (S-FARCO), which is able to process several hundred thousand frames per second. The principal advantage of our new system is its wide applicability to any correlation scheme. Three different configurations were proposed, each depending on correlation speed. This paper describes and evaluates a software correlation filter. The face recognition function proved highly accurate, seeing that a low-resolution facial image size (64 × 64 pixels) has been successfully implemented. An operation speed of less than 10 ms was achieved using a personal computer with a central processing unit (CPU) of 3 GHz and 2 GB memory. When we applied the software correlation filter to a high-security cellular phone face recognition system, experiments on 30 female students over a period of three months yielded low error rates: 0% false acceptance rate and 2% false rejection rate. Therefore, the filtering correlation works effectively when applied to low resolution images such as web-based images or faces captured by a monitoring camera.

  19. Efficient face recognition using local derivative pattern and shifted phase-encoded fringe-adjusted joint transform correlation

    NASA Astrophysics Data System (ADS)

    Biswas, Bikram K.; Alam, Mohammad S.; Chowdhury, Suparna

    2016-04-01

    An improved shifted phase-encoded fringe-adjusted joint transform correlation technique is proposed in this paper for face recognition which can accommodate the detrimental effects of noise, illumination, and other 3D distortions such as expression and rotation variations. This technique utilizes a third order local derivative pattern operator (LDP3) followed by a shifted phase-encoded fringe-adjusted joint transform correlation (SPFJTC) operation. The local derivative pattern operator ensures better facial feature extraction in a variable environment while the SPFJTC yields robust correlation output for the desired signals. The performance of the proposed method is determined by using the Yale Face Database, Yale Face Database B, and Georgia Institute of Technology Face Database. This technique has been found to yield better face recognition rate compared to alternate JTC based techniques.

  20. Face familiarity promotes stable identity recognition: exploring face perception using serial dependence

    PubMed Central

    Kok, Rebecca; Van der Burg, Erik; Rhodes, Gillian; Alais, David

    2017-01-01

    Studies suggest that familiar faces are processed in a manner distinct from unfamiliar faces and that familiarity with a face confers an advantage in identity recognition. Our visual system seems to capitalize on experience to build stable face representations that are impervious to variation in retinal input that may occur due to changes in lighting, viewpoint, viewing distance, eye movements, etc. Emerging evidence also suggests that our visual system maintains a continuous perception of a face's identity from one moment to the next despite the retinal input variations through serial dependence. This study investigates whether interactions occur between face familiarity and serial dependence. In two experiments, participants used a continuous scale to rate attractiveness of unfamiliar and familiar faces (either experimentally learned or famous) presented in rapid sequences. Both experiments revealed robust inter-trial effects in which attractiveness ratings for a given face depended on the preceding face's attractiveness. This inter-trial attractiveness effect was most pronounced for unfamiliar faces. Indeed, when participants were familiar with a given face, attractiveness ratings showed significantly less serial dependence. These results represent the first evidence that familiar faces can resist the temporal integration seen in sequential dependencies and highlight the importance of familiarity to visual cognition. PMID:28405355