Deep nonlinear metric learning with independent subspace analysis for face verification

• Authors: Xinyuan Cai;Chunheng Wang;Baihua Xiao;Xue Chen;Ji Zhou
• Venue: Proceedings of the 20th ACM international conference on Multimedia
• Year: 2012

Face verification is the task of determining by analyzing face images, whether a person is who he/she claims to be. It is a very challenge problem, due to large variations in lighting, background, expression, hairstyle and occlusion. The crucial problem is to compute the similarity of two face vectors. Metric learning has provides a viable solution to this problem. Until now, many metric learning algorithms have been proposed, but they are usually limited to learning a linear transformation (i.e. finding a global Mahalanobis metric). In this brief, we propose a nonlinear metric learning method, which learns an explicit mapping from the original space to an optimal subspace, using deep Independent Subspace Analysis network. Compared to kernel methods, which can also learn nonlinear transformations, our method is a deep and local learning architecture, and therefore exhibits more powerful ability to learn the nature of highly variable dataset. We evaluate our method on the LFW benchmark, and results show very comparable performance to the state-of-art methods (achieving 92.28% accuracy), while maintaining simplicity and good generalization ability.