Real-Time Face Recognition Using Gram-Schmidt Orthogonalization for LDA

Authors:
Wenming Zheng;Cairong Zou;Li Zhao
Affiliations:
Southeast University, Nanjing, P.R. China;Southeast University, Nanjing, P.R. China;Southeast University, Nanjing, P.R. China
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Year:
2004

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Abstract

A real-time face recognition method using Gram-Schmidt Orthogonalization for linear discriminant analysis (GSLDA) is presented in this paper. The GSLDA algorithm avoids the large matrices computation such as computing the inverse or diagonalization of matrices, which may be somewhat problematic in terms of computational demands and numerical accuracy. On the other hand, GSLDA also achieves better recognition performance than the classical linear discriminant analysis (LDA) by overcoming the degenerate eigenvalue problem of LDA. Experimental results on real face databases have confirmed the better performance of the proposed method.