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1 May 2005 Component-based subspace linear discriminant analysis method for face recognition with one training sample
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Many face recognition algorithms/systems have been developed in the last decade and excellent performances have also been reported when there is a sufficient number of representative training samples. In many real-life applications such as passport identification, only one well-controlled frontal sample image is available for training. Under this situation, the performance of existing algorithms will degrade dramatically or may not even be implemented. We propose a component-based linear discriminant analysis (LDA) method to solve the one training sample problem. The basic idea of the proposed method is to construct local facial feature component bunches by moving each local feature region in four directions. In this way, we not only generate more samples with lower dimension than the original image, but also consider the face detection localization error while training. After that, we propose a subspace LDA method, which is tailor-made for a small number of training samples, for the local feature projection to maximize the discrimination power. Theoretical analysis and experiment results show that our proposed subspace LDA is efficient and overcomes the limitations in existing LDA methods. Finally, we combine the contributions of each local component bunch with a weighted combination scheme to draw the recognition decision. A FERET database is used for evaluating the proposed method and results are encouraging.
©(2005) Society of Photo-Optical Instrumentation Engineers (SPIE)
Jian Huang, Pong C. Yuen, Wen-Sheng Chen, and J. H. Lai "Component-based subspace linear discriminant analysis method for face recognition with one training sample," Optical Engineering 44(5), 057002 (1 May 2005).
Published: 1 May 2005


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