3D Assisted Face Recognition: Dealing With Expression Variations
One of the most critical sources of variation in face recognition is facial expressions, especially in the frequent case where only a single sample per person is available for enrollment. Methods that improve the accuracy in the presence of such variations are still required for a reliable authentic...
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| Veröffentlicht in: | IEEE transactions on information forensics and security 2014-05, Vol.9 (5), p.826-838 |
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| container_title | IEEE transactions on information forensics and security |
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| creator | Erdogmus, Nesli Dugelay, Jean-Luc |
| description | One of the most critical sources of variation in face recognition is facial expressions, especially in the frequent case where only a single sample per person is available for enrollment. Methods that improve the accuracy in the presence of such variations are still required for a reliable authentication system. In this paper, we address this problem with an analysis-by-synthesis-based scheme, in which a number of synthetic face images with different expressions are produced. For this purpose, an animatable 3D model is generated for each user based on 17 automatically located landmark points. The contribution of these additional images in terms of the recognition performance is evaluated with three different techniques (principal component analysis, linear discriminant analysis, and local binary patterns) on face recognition grand challenge and Bosphorus 3D face databases. Significant improvements are achieved in face recognition accuracies, for each database and algorithm. |
| doi_str_mv | 10.1109/TIFS.2014.2309851 |
| format | Article |
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Methods that improve the accuracy in the presence of such variations are still required for a reliable authentication system. In this paper, we address this problem with an analysis-by-synthesis-based scheme, in which a number of synthetic face images with different expressions are produced. For this purpose, an animatable 3D model is generated for each user based on 17 automatically located landmark points. The contribution of these additional images in terms of the recognition performance is evaluated with three different techniques (principal component analysis, linear discriminant analysis, and local binary patterns) on face recognition grand challenge and Bosphorus 3D face databases. 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Methods that improve the accuracy in the presence of such variations are still required for a reliable authentication system. In this paper, we address this problem with an analysis-by-synthesis-based scheme, in which a number of synthetic face images with different expressions are produced. For this purpose, an animatable 3D model is generated for each user based on 17 automatically located landmark points. The contribution of these additional images in terms of the recognition performance is evaluated with three different techniques (principal component analysis, linear discriminant analysis, and local binary patterns) on face recognition grand challenge and Bosphorus 3D face databases. Significant improvements are achieved in face recognition accuracies, for each database and algorithm.</description><subject>Accuracy</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Biometrics</subject><subject>Computer science; control theory; systems</subject><subject>Computer systems and distributed systems. User interface</subject><subject>Data processing. List processing. Character string processing</subject><subject>Discriminant analysis</subject><subject>Exact sciences and technology</subject><subject>Face</subject><subject>Face recognition</subject><subject>Lighting</subject><subject>Memory organisation. Data processing</subject><subject>Nose</subject><subject>Pattern recognition</subject><subject>Pattern recognition. Digital image processing. 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| source | IEEE Electronic Library (IEL) |
| subjects | Accuracy Applied sciences Artificial intelligence Biometrics Computer science control theory systems Computer systems and distributed systems. User interface Data processing. List processing. Character string processing Discriminant analysis Exact sciences and technology Face Face recognition Lighting Memory organisation. Data processing Nose Pattern recognition Pattern recognition. Digital image processing. Computational geometry Shape Software Solid modeling Three dimensional Three dimensional models Three-dimensional displays |
| title | 3D Assisted Face Recognition: Dealing With Expression Variations |
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