Locality repulsion projections for image-to-set face recognition

Conventional face recognition usually assumes that both the training and test phases employ the same form of data in a face recognition system. In many real world face recognition applications such as e-passport and ID card identification, it is common to have only a single sample per person enrolled or recorded in these systems because it is generally difficult to collect additional samples for training. In the testing phase, however, the probe samples are usually captured on spot and it is possible to collect a number of face samples for recognition. This problem is defined as image-to-set face recognition in this paper, which is essentially different from most existing image-to-image or set-to-set face recognition problems. Specifically, we propose in this paper a new locality repulsion projections (LRP) method to address this problem. Motivated by the fact that interclass face samples with higher similarity usually lie in a locality and are more easily misclassified than those with lower similarity, we aim to learn a mapping to project the original face samples onto a low-dimensional feature subspace such that samples lying in a locality are repulsed and more discriminative information can be exploited for recognition. To better characterize the similarity between a gallery sample and a testing set, we further propose a reconstruction-based point-to-set similarity measure to identify the unlabeled subjects. Experimental results on two widely used face databases are presented to demonstrate the efficacy of the proposed method.