Facial recognition with singular value decomposition

Abstract

This paper implements a real-time system to recognize faces. The approach is essentially to apply the concepts of vector space and subspace to face recognition. The set of known faces with m × n pixels forms a subspace, called "face space", of the "image space" containing all images with m × n pixels. This face space best defines the variation of the known faces. The basis of the face space is defined by the singularvectors of the set of known faces. These singular-vectors do not necessarily correspond to the distinct features like ears, eyes and noses. The projection of a new image onto this face space is then compared to the available projections of known faces to identify the person. Since the dimension of face subspace is much less than the whole image space, it is much easier to compare projections than origin images pixel by pixel. Based on the above idea, a Singular Value Decomposition (SVD) approach is implemented in this paper. The framework provides our system the ability to learn to recognize new faces in a real-time and automatic manner. © 2007 Springer.