Making FLDA applicable to face recognition with one sample per person

In face recognition, the Fisherface approach based on Fisher linear discriminant analysis (FLDA) has obtained some success. However, FLDA fails when each person just has one training face sample available because of nonexistence of the intra-class scatter. In this paper, we propose to partition each face image into a set of sub-images with the same dimensionality, therefore obtaining multiple training samples for each class, and then apply FLDA to the set of newly produced samples. Experimental results on the FERET face database show that the proposed approach is feasible and better in recognition performance than E(PC)²A.