Wavelet Kernel Construction for Kernel Discriminant Analysis on Face Recognition

Kernel Discriminant Analysis (KDA) has been shown to be one of the promising approaches to handle the pose and illumination problem in face recognition. However, empirical results show that the performance for KDA on face recognition is sensitive to the kernel function and its parameters. Instead of following existing KDA methods in selecting popular kernel function, this paper proposes a new approach for constructing kernel using wavelet. By virtue of cubic B spline function, wavelet kernel function is constructed. A wavelet kernel based subspace linear discriminant (WKSLDA) algorithm is then developed for face recognition. Two human face databases, namely FERET and CMU PIE databases, are selected for evaluation. The results are encouraging. Comparing with the existing state-of-the-art RBF kernel based LDA methods, the proposed method gives superior resu

I. Introduction

Linear (Fisher) discriminant analysis-based (LDA) [1] method has been shown to be an effective approach in face recognition application and its superior performance has been reported in many literatures [3]–[14] in the last decade. LDA is theoretically sound and its objective is to find the most discriminant feature for classification. Hence, it is good for pattern recognition problem. However, LDA suffers from two major drawbacks. First, LDA is a linear method and is hard to solve nonlinear problem, while the second is the small sample size (S3) problem.