Discriminative multi-manifold analysis for face recognition from a single training sample per person

Conventional appearance-based face recognition methods usually assume there are multiple samples per person (MSPP) available during the training phase for discriminative feature extraction. In many practical face recognition applications such as law enhancement, e-passport and ID card identification, this assumption, however, may not hold as there is only a single sample per person (SSPP) enrolled or recorded in these systems. Many popular face recognition methods fail to work well in this scenario because there are not enough samples for discriminant learning. To address this problem, we propose in this paper a novel discriminative multi-manifold analysis (DMMA) method by learning discriminative features from image patches. First, we partition each enrolled image into several non-overlapping patches to form an image set for each sample per person. Then, we formulate the SSPP face recognition as a manifold-manifold matching problem and learn multiple DMMA feature spaces to maximize the manifold margins of different persons. Lastly, we propose a reconstruction-based manifold-manifold distance to identify the unlabeled subjects. Experimental results on three widely used face databases are presented to demonstrate the efficacy of the proposed approach.