LETTER Preserving Complete Subspace Structure Projection for Face Recognition
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Neural Information Processing – Letters and Reviews Vol. 11, No. 2, February 2007 25 Preserving Complete Subspace Structure Projection for Face Recognition Jun-Bao Li Department of Automatic Test and Control Harbin Institute of Technology, Harbin, 150001,P.R. China Phone: +86-451-6413531-8603, Fax: +86-451-6418083, E-mail: junbaolihit@hotmail.com
Jeng-Shyang Pan Department of Electronic Engineering National Kaohsiung University of Applied Sciences, D415 Chien-Kung Road, Kaohsiung 807, Taiwan Phone: +886-7-3814526 Ext. 5636，Fax:+886-7-3811182，Email: jspan@cc.kuas.edu.tw
(Submitted on November 1, 2006)
Abstract — Subspace-based face recognition is one of the most successful methods for face recognition. Eigenfaces, Fisherfaces, and Laplacianfaces methods, which are based on PCA, LPP and LDA that preserve global, local and cluster structure information respectively, are three representative methods of subspace-based face recognition approaches. In this paper, we propose a novel pattern classification namely Preserving Complete Subspace Structure Projection (PCSSP) for face recognition. First we analyze their contributions of extracting the discriminating information respectively firstly, and then we construct a 3D parameter space using three subspace dimensions as axes. We can take advantage of the global, local and cluster structure information provided by three subspaces through searching over the whole 3D parameter space instead of searching only in lines or local regions as the standard subspace methods. Finally based on the 3D parameter space, we propose a framework for PCA, LPP and LDA. The experimental results with the ORL and Yale face databases show that the proposed algorithm outperforms three standard subspace approaches, and the proposed algorithm can also improve the computational efficiency without influencing the recognition performance.
Keywords — Preserving complete subspace structure projection, face recognition, principal component analysis, linear discriminant analysis, locality preserving projections, 3D parameter space
1. Introduction Face recognition has become a very active research area in recent years due to its wide applications. Many approaches have been developed in the last years, and good surveys can be found in [8], [9], and [10]. Among various face recognition algorithms, one of the most successful techniques is the appearance-based method. Among the crucial issues of face recognition technology, the low-dimensional feature representation with enhanced discriminatory power is of paramount importance in face recognition systems. To resolve the too large dimension problem when using original face images, dimensionality reduction techniques are employed widely [1], [2].Two of the most popular algorithms of these dimensionality reduction techniques are Principal Component Analysis (PCA) [1] and Linear Discriminant Analysis (LDA) [2]. Additionally, Laplacianfaces [11] method based on PCA and Locality Preserving Projections (LPP) is another successful face recognition method. Recently, the nonlinear methods, KPCA [7] and KFD [3], [4], have been widely used since kernel machine techniques [5], [6] were applied to the face recognition. Especially, PCA aims to preserve the global structure, and LPP preserves local structure information, and LDA preserves the cluster structure. In order to take full advantage of all structure information, we construct a 3D parameter space using the three subspace dimensions as axes in this paper. In the 3D parameter space, all above three methods search in the lines or plane only, in
LETTER Preserving Complete Subspace Structure Projection for Face Recognition Jun-Bao Li and Jeng-Shyang Pan 26other words, we only apply one kind of structure information when we only apply one of LPP, PCA and LDA. In our algorithm, we can search the optimal parameters through the 3D parameter space for apply three kinds of structure information enough, so it is reasonable to enhance the recognition performance with searching over 3D parameter space instead of only in lines or planes as three standard subspace methods. The remainder of this paper is organized as follows. The proposed algorithm is introduced in Section 2. In Section 3, experiments with the Yale and ORL face databases are presented to demonstrate the effectiveness of proposed algorithm. Conclusions are summarized in Section 4.
