Face Recognition by Support Vector Machines
Guodong Guo, Stan Z. Li, Kapluk Chan
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.
Face recognition, support vector machines, optimal
separating hyperplane, binary tree, eigenface, principal