Gabor Features and Support Vector Machine for Face Identification( BIOMETRICS AND ITS APPLICATIONS)

Abstract: A Support Vector Machine (SVM) face identification method using optimized Gabor fea-tures is presented in this paper. 200 Gabor features are first selected by a boosting algorithm, which are then combined with SVM to build a two-class based face recognition system. While com-putation and memory cost of the Gabor feature extraction process has been significantly reduced, our method has achieved the same accuracy as a Gabor feature and LDA based multi-class system. Keywords Gabor features, Support Vector Machine, Face identification. 1. Introduction Automatic recognition of human faces has been an active research area in recent years because it is user-friendly and unintrusive, and unlike fin-gerprint or iris recognition, it does not require ela-brated collaboration of the users. In addition to the importance of advancing research, it has a number of commercial and law-enforcement applications such as surveillance, security, telecommunications and human-computer intelligent interaction. Gabor wavelet based recognition algorithms have been shown to be advantageous over many other methods in the literature. For example, the Elastic Bunch Graph Matching (EBGM) algorithm has shown very competitive performance and was ranked the top performer in the FERET evaluation [1]. In a recent face verification competition (FVC2004), both above methods used Gabor wave-lets for feature extraction. The application of Gabor wavelets for face recognition was pioneered by Lades et al.’s work since Dynamic Link Architec-ture (DLA) was proposed in 1993 [2]. In this system, faces are represented by a rectangular graph with local features extracted at the nodes using Gabor wavelets, referred to as Gabor jets. Wiskott et al [3] extended DLA to EBGM, where graph nodes are located at a number of facial landmarks. Since then, a large number of elastic graph based methods have been proposed [4-7]. Chung et al. [8] use the Gabor wavelet responses over a set of 12 fiducial points as input to a Principal Componet Analysis (PCA) algorithm, yielding a feature vector of 480 components. They claim to have improved the recognition rate up to 19% with this method compared to that by a raw PCA. All of these me-thods can be classified as analytic approaches since the local features extracted from selected points in faces are used for recognition. Recently, Gabor wavelets have also been applied in global form for face recognition [9, 10]. Liu et al. [9] vectorize the Gabor responses and then apply a downsampling by a factor of 64 to reduce the computation cost of the following subspace training. Their Gabor-based enhanced Fisher linear discriminant model outper-forms Gabor PCA and Gabor Fisherfaces. These holistic methods normally use the whole image after Gabor wavelets processing for feature representa-tion. A more detailed survey on Gabor wavelet based face recognition methods can be found in [11]. Despite the success of Gabor wavelets based face recognition systems, both the feature extraction process and the huge dimension of Gabor features extracted demands large computation and memory costs, which makes them impractical for real appli-cations [11]. For the same reason, Support Vector Machine (SVM) has also seldom been applied to face recognition using Gabor features. While