Eigenfaces for Recognition

We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.

[1]  L. D. Harmon,et al.  Identification of human faces , 1971 .

[2]  L. D. Harmon Some Aspects of Recognition of Human Faces , 1971 .

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

[4]  Martin A. Fischler,et al.  The Representation and Matching of Pictorial Structures , 1973, IEEE Transactions on Computers.

[5]  S. Carey,et al.  From piecemeal to configurational representation of faces. , 1977, Science.

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

[7]  A. Young,et al.  The human face , 1982 .

[8]  A. W. Ellis Normality and pathology in cognitive functions , 1982 .

[9]  R. Desimone,et al.  Stimulus-selective properties of inferior temporal neurons in the macaque , 1984, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[10]  E M Critchley,et al.  The human face. , 1985, British medical journal.

[11]  T. J. Stonham,et al.  Practical Face Recognition and Verification with Wisard , 1986 .

[12]  L Sirovich,et al.  Low-dimensional procedure for the characterization of human faces. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[13]  Ian Craw,et al.  Automatic extraction of face-features , 1987, Pattern Recognit. Lett..

[14]  A. J. Mistlin,et al.  Visual neurones responsive to faces , 1987, Trends in Neurosciences.

[15]  Alice J. O'Toole,et al.  A physical system approach to recognition memory for spatially transformed faces , 1988, Neural Networks.

[16]  H. Midorikawa,et al.  The face pattern identification by back-propagation learning procedure , 1988, Neural Networks.

[17]  Peter J. Burt,et al.  Smart sensing within a pyramid vision machine , 1988, Proc. IEEE.

[18]  P.W.M. Tsang,et al.  A system for recognising human faces , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[19]  M. K. Fleming,et al.  Categorization of faces using unsupervised feature extraction , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

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