A feature based approach to face recognition

A feature-based approach to face recognition in which the features are derived from the intensity data without assuming any knowledge of the face structure is presented. The feature extraction model is biologically motivated, and the locations of the features often correspond to salient facial features such as the eyes, nose, etc. Topological graphs are used to represent relations between features, and a simple deterministic graph-matching scheme that exploits the basic structure is used to recognize familiar faces from a database. Each of the stages in the system can be fully implemented in parallel to achieve real-time recognition. Experimental results for a 128*128 image with very little noise are evaluated.<<ETX>>

[1]  D H HUBEL,et al.  RECEPTIVE FIELDS AND FUNCTIONAL ARCHITECTURE IN TWO NONSTRIATE VISUAL AREAS (18 AND 19) OF THE CAT. , 1965, Journal of neurophysiology.

[2]  S. Zucker,et al.  Endstopped neurons in the visual cortex as a substrate for calculating curvature , 1987, Nature.

[3]  J. G. Daugman.  Relaxation neural network for nonorthogonal image transforms , 1988, IEEE 1988 International Conference on Neural Networks.

[4]  Yehoshua Y. Zeevi,et al.  The Generalized Gabor Scheme of Image Representation in Biological and Machine Vision , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[6]  R. von der Heydt,et al.  Mechanisms of contour perception in monkey visual cortex. I. Lines of pattern discontinuity , 1989, The Journal of neuroscience : the official journal of the Society for Neuroscience.

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

[8]  T. Yoshida,et al.  Department of Information Science and Intelligent Systems , 1989 .

[9]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  R. Chellappa,et al.  Passive Navigation in a partially known environment , 1991, Proceedings of the IEEE Workshop on Visual Motion.

[11]  B. S. Manjunath,et al.  Balloon motion estimation using two frames , 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

[12]  B. S. Manjunath,et al.  A computational approach to boundary detection , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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