Multi-source Domain Adaptation for Face Recognition

Abstract

For transfer learning, many research works have demonstrated that effective use of information from multi-source domains will improve classification performance. In this paper, we propose a method of Targetize Multi-source Domain Bridged by Common Subspace (TMSD) for face recognition, which transfers rich supervision knowledge from more than one labeled source domains to the unlabeled target domain. Specifically, a common subspace is learnt for several domains by keeping the maximum total correlation. In this way, the discrepancy of each domain is reduced, and the structures of both the source and target domains are well preserved for classification. In the common subspace, each sample projected from the source domains is sparsely represented as a linear combination of several samples projected from the target domain, such that the samples projected from different domains can be well interlaced. Then, in the original image space, each source domain image can be represented as a linear combination of neighbors in the target domain. Finally, the discriminant subspace can be obtained by targetized multi-source domain images using supervised learning algorithm. The experimental results illustrate the superiority of TMSD over those competitive ones.