Face Recognition by Support Vector Machines

Guodong Guo, Stan Z. Li, Kapluk Chan

Abstract

Support Vector Machines (SVMs) have been recently proposed as a new technique for pattern recognition. In this paper, the SVMs with a binary tree recognition strategy are used to tackle the face recognition problem. We illustrate the potential of SVMs on the Cambridge ORL face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details. We also present the recognition experiment on a larger face database of 1079 images of 137 individuals. We compare the SVMs based recognition with the standard eigenface approach using the Nearest Center Classification (NCC) criterion. Keywords: Face recognition, support vector machines, optimal separating hyperplane, binary tree, eigenface, principal component analysis.