Face recognition using LDA-based algorithms

Low-dimensional feature representation with enhanced discriminatory power is of paramount importance to face recognition (FR) systems. Most of traditional linear discriminant analysis (LDA)-based methods suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the "small sample size" (SSS) problem which is often encountered in FR tasks. In this paper, we propose a new algorithm that deals with both of the shortcomings in an efficient and cost effective manner. The proposed method is compared, in terms of classification accuracy, to other commonly used FR methods on two face databases. Results indicate that the performance of the proposed method is overall superior to those of traditional FR approaches, such as the eigenfaces, fisherfaces, and D-LDA methods.

I. Introduction

Feature selection for face representation is one of central issues to face recognition (FR) systems. Among various solutions to the problem (see [1], [2] for a survey), the most successful seems to be those appearance-based approaches, which generally operate directly on images or appearances of face objects and process the images as two-dimensional (2-D) holistic patterns, to avoid difficulties associated with three-dimensional (3-D) modeling, and shape or landmark detection [2]. Principle component analysis (PCA) and linear discriminant analysis (LDA) are two powerful tools used for data reduction and feature extraction in the appearance-based approaches. Two state-of-the-art FR methods, Eigenfaces [3] and Fisherfaces [4], built on the two techniques, respectively, have been proved to be very successful.