Automatic Face Recognition: What Representation?

A testbed for automatic face recognition shows an eigenface coding of shape-free texture, with manually coded landmarks, was more effective than correctly shaped faces, being dependent upon high-quality representation of the facial variation by a shape-free ensemble. Configuration also allowed recognition, these measures combine to improve performance and allowed automatic measurement of the face-shape. Caricaturing further increased performance. Correlation of contours of shapefree images also increased recognition, suggesting extra information was available. A natural model considers faces as in a manifold, linearly approximated by the two factors, with a separate system for local features.

[1]  G. Rhodes,et al.  Identification and ratings of caricatures: Implications for mental representations of faces , 1987, Cognitive Psychology.

[2]  I. Craw,et al.  Spatial Content and Spatial Quantisation Effects in Face Recognition , 1994, Perception.

[3]  Ian Craw,et al.  Testing face recognition systems , 1994, Image Vis. Comput..

[4]  Timothy F. Cootes,et al.  An Automatic Face Identification System Using Flexible Appearance Models , 1994, BMVC.

[5]  A. O'Toole,et al.  Structural aspects of face recognition and the other-race effect , 1994, Memory & cognition.

[6]  S. Ullman Aligning pictorial descriptions: An approach to object recognition , 1989, Cognition.

[7]  Ian Craw,et al.  Face Recognition by Computer , 1992, BMVC.

[8]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[9]  Geoffrey E. Hinton,et al.  Parallel Models of Associative Memory , 1989 .

[10]  Rajesh P. N. Rao,et al.  Natural Basis Functions and Topographic Memory for Face Recognition , 1995, IJCAI.

[11]  Ian Craw,et al.  Finding Face Features , 1992, ECCV.

[12]  Leslie S. Smith,et al.  The principal components of natural images , 1992 .

[13]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Timothy F. Cootes,et al.  Active shape models , 1998 .

[15]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[16]  D. Perrett,et al.  Visual Processing of Facial Distinctiveness , 1994, Perception.

[17]  Teuvo Kohonen,et al.  Storage and Processing of Information in Distributed Associative Memory Systems , 1981 .

[18]  Shimon Edelman,et al.  Learning to Recognize Faces from Examples , 1992, ECCV.

[19]  Hiroshi Harashima,et al.  Basis generation and description of facial images using principal-component analysis , 1997, Systems and Computers in Japan.

[20]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[21]  Lawrence Sirovich,et al.  Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  R. Watt A Computational Examination of Image Segmentation and the Initial Stages of Human Vision , 1994, Perception.

[24]  W. J. Welsh,et al.  Classification of facial features for recognition , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.