A kernel machine based approach for multi-view face recognition

Techniques that can introduce low-dimensional feature representation with enhanced discriminatory power is of paramount importance in face recognition applications. It is well known that the distribution of face images, under a perceivable variation in viewpoint, illumination or facial expression, is highly nonlinear and complex. It is therefore, not surprising that linear techniques, such as those based on Principle Component Analysis (PCA) or Linear Discriminant Analysis (LDA) cannot provide reliable and robust solutions to those complex face recognition problems. In this paper, we propose a kernel machine based discriminant analysis method, which deals with the nonlinearity of the face patterns' distribution. The proposed method also effectively solves the "small sample size" (SSS) problem which exists in most face recognition tasks. The new algorithm has been tested, in terms of error rate performance, on the multi-view UMIST Face Database. Results indicate that the proposed methodology outperform other commonly used approaches, such as the Kernel-PCA (KPCA) and the Generalized Discriminant Analysis (GDA).