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Journal of Information Science and Engineering, Vol. 25 No. 2, pp. 419-433 (March 2009)

Orthogonal Locality Sensitive Discriminant Analysis for Face Recognition*

Yi Jin and Qiu-Qi Ruan
Institute of Information Science
Beijing Jiaotong University
Beijing, 100044 P.R. China

An innovative appearance-based method that called Orthogonal Locality Sensitive Discriminant Analysis is presented for face recognition in this paper. Our algorithm is based on the Locality Sensitive Discriminant Analysis (LSDA) algorithm, which aims at finding a projection by maximizing the margin between data points from different classes at each local area. However, a major disadvantage of LSDA is that LSDA is non- orthogonal, and this makes it difficult to estimate the intrinsic dimensionality and to reconstruct the face data. Non-orthogonality distorts the local geometrical structure of the data submanifold. In this paper, an Orthogonal LSDA algorithm is proposed to preserve the local geometrical structure by computing the mutually orthogonal basis functions iteratively. Since it produces orthogonal basis functions and can have more local structure preserving power, it is expected to have more discriminating power than LSDA. Experiments based on the ORL and Yale face database show the impressive performance of the proposed method. Results show that our new algorithm outperforms the other popular approaches reported in the literature and achieves a much higher recognition rate.

Keywords: face recognition, locality preserving projections, locality sensitive discriminant analysis (LSDA), orthogonal basis functions, orthogonal locality Sensitive discriminant analysis (OLSDA)

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Received August 31, 2007; revised January 7 & April 23, 2008; accepted May 2, 2008.
Communicated by Mark H. Y. Liao.
* This paper was supported partly by the National Natural Science Foundation of China under grants No. 60472033 and No. 60672062.