Single Sample Face Recognition via Learning Deep Supervised Auto-Encoders

This paper targets learning robust image representation for single training sample per person face recognition. Motivated by the success of deep learning in image representation, we propose a supervised auto-encoder, which is a new type of building block for deep architectures. There are two features distinct our supervised auto-encoder from standard auto-encoder. First, we enforce the faces with variants to be mapped with the canonical face of the person, for example, frontal face with neutral expression and normal illumination; Second, we enforce features corresponding to the same person to be similar. As a result, our supervised auto-encoder extracts the features which are robust to variances in illumination, expression, occlusion, and pose, and facilitates the face recognition. We stack such supervised auto-encoders to get the deep architecture and use it for extracting features in image representation. Experimental results on the AR, Extended Yale B, CMU-PIE, and Multi-PIE datasets demonstrate that by coupling with the commonly used sparse representation based classification, our stacked supervised auto-encoders based face representation significantly outperforms the commonly used image representations in single sample per person face recognition, and it achieves higher recognition accuracy compared with other deep learning models, including the deep Lambertian network, in spite of much less training data and without any domain information. Moreover, supervised auto-encoder can also be used for face verification, which further demonstrates its effectiveness for face representation.