Sampled Two-Dimensional LDA for Face Recognition with One Training Image per Person

The two-dimensional linear discriminant analysis (2DLDA) is one of the most successful face recognition methods. However, it cannot be directly applied to the face recognition where only one sample image per person is available for training. In this paper, we present a new method based on 2DLDA to deal with the single training sample problem. The method derive a set of sub-images from a single face image by sampling, therefore obtaining multiple training samples for each class, and then apply 2DLDA to the set of newly produced samples. The proposed algorithms are compared with both the E(PC)²A algorithm and the SVD perturbation algorithm which is proposed for addressing the single training sample problem. Experimental results on the ORL face database show that the proposed approach is feasible and has higher recognition performance than E(PC)²A and SVD perturbation algorithms