Optimal FLD algorithm for facial feature extraction

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Abstract
In this paper, we try to extend Fisher linear discriminant analysis (FLD) to the singular cases. Firstly, PCA is used to reduce the dimension of feature space to N-1 (N denotes the number of training samples). Then, the transformed space is divided into two subspaces: the null space of within- class scatter matrix and its orthogonal complement, from which two cases of optimal discriminant vectors are selected respectively. Finally, we test our method on ORL face database, and achieve a recognition rate of 97% with a minimum distance classifier or a nearest neighbor classifier. The experimental results indicate that our approach is better than classical Eigenfaces and Fisherfaces with respect to recognition performance.

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