Unsupervised feature learning via prior information-based similarity metric learning for face verification

摘要

Here, an efficient framework is developed to address the problem of unconstrained face verification. In particular, an unsupervised feature learning method for face image representation and a novel similarity metric model are discussed. First, the authors propose an unsupervised feature learning method with sparse auto-encoder (SAE) based on local descriptor (SAELD). A set of filter operators are learned based on SAE model from local patches, and face descriptors are extracted by applying the set of filter operators to convolve images. This can address the face discriminative representation issue of unconstrained face verification. Then pairwise SAELD descriptors are projected into the weighted subspace. Furthermore, a prior information-based similarity metric learning model is presented, in which the metric matrix is learned by enforcing a regularisation term based on the prior similar and discriminative information. This idea can improve the robustness to intra-personal variations and discrimination to inter-personal variations. Experimental results show that the proposed method has competitive performance compared with several state-of-the-art methods on challenging labelled faces in the wild data set.