Orientation competition in cortical filters - an application to face recognition

A biologically motivated, computationally intensive approach to computer vision is developed and applied to the problem of automatic face recognition. The approach is based on the use of two-dimensional Gabor functions which model the receptive eld functions of simple cells in the primary visual cortex of mammals. The convolutions of an input image with a set of antisymmetric visual receptive eld functions (imaginary parts of Gabor functions) become the subject of thresholding and orientation competition. The developed cortical lters deliver highly structured information which is used for eecient feature extraction and representation in a lower dimension space. Applied to face recognition, the method gives a recognition rate of 98.5% on a large database of face images.

[1]  Zi-Quan Hong,et al.  Algebraic feature extraction of image for recognition , 1991, Pattern Recognit..

[2]  D. Hubel,et al.  Sequence regularity and geometry of orientation columns in the monkey striate cortex , 1974, The Journal of comparative neurology.

[3]  C. von der Malsburg,et al.  Distortion invariant object recognition by matching hierarchically labeled graphs , 1989, International 1989 Joint Conference on Neural Networks.

[4]  J. Bigun.  Gabor Phase in Boundary Tracking and Region Segregation , 1993 .

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

[6]  A. Yuille.  Deformable Templates for Face Recognition , 1991, Journal of Cognitive Neuroscience.

[7]  S Marcelja,et al.  Mathematical description of the responses of simple cortical cells. , 1980, Journal of the Optical Society of America.

[8]  A. Young,et al.  Handbook of Research on Face Processing , 1989 .

[9]  Nicolai Petkov,et al.  Systolic Parallel Processing , 1992 .

[10]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[11]  John G. Daugman,et al.  Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression , 1988, IEEE Trans. Acoust. Speech Signal Process..

[12]  D. Pollen,et al.  Phase relationships between adjacent simple cells in the visual cortex. , 1981, Science.

[13]  Emmanuel Viennet,et al.  Solving the Human Face Recognition Task using Neural Nets , 1992 .

[14]  Y. Kaya,et al.  A BASIC STUDY ON HUMAN FACE RECOGNITION , 1972 .

[15]  Osamu Nakamura,et al.  Identification of human faces based on isodensity maps , 1991, Pattern Recognit..

[16]  D. V. van Essen,et al.  The representation of the visual field in parvicellular and magnocellular layers of the lateral geniculate nucleus in the macaque monkey , 1984, The Journal of comparative neurology.

[17]  Nicolai Petkov,et al.  Lateral inhibition in cortical filters , 1993 .

[18]  Vicki Bruce,et al.  COMPUTER RECOGNITION OF FACES , 1989 .

[19]  Rama Chellappa,et al.  A feature based approach to face recognition , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.