Component-based LDA Method for Face Recognition with One Training Sample

Authors:
Jian Huang;Pong C. Yuen;Wen-Sheng Chen;J. H. Lai
Affiliations:
-;-;-;-
Venue:
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Year:
2003

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Abstract

Many face recognition algorithms/systems have been developed in thelast decade and excellent performances are also reported when thereis sufficient number of representative training samples. In manyreal-life applications, only one training sample is available.Under this situation, the performance of existing algorithms willbe degraded dramatically or the formulation is incorrect, which inturn, the algorithm cannot be implemented. In this paper, wepropose a component-based linear discriminant analysis (LDA) methodto solve the one training sample problem. The basic idea of theproposed method is to construct local facial feature componentbunches by moving each local feature region in four directions. Inthis way, we not only generate more samples, but also consider theface detection localization error while training. After that, weemploy a sub-space LDA method, which is tailor-made for smallnumber of training samples, for the local feature projection tomaximize the discrimination power. Finally, combining thecontributions of each local feature draws the recognition decision.FERET database is used for evaluating the proposed method andresults are encouraging.