Multiview max-margin subspace learning for cross-view gait recognition

Cross-view gait recognition can be regarded as a domain adaption problem, in which, probe gait to be recognized in one view is different from gallery gaits collected in another view. In this paper, we present a subspace learning based method, called Multiview Max-Margin Subspace Learning (MMMSL), to learn a common subspace for associating gait data across different views. A group of projection matrices that respectively map data from different views into the common subspace are optimized via simultaneously minimizing the within-class variations and maximizing the local between-class variations of the low-dimensional embeddings from both inter-view and intra-view. In the learnt subspace, same-class samples from all views cluster together, and each different-class cluster is kept away from its nearest neighbors as far as possible. Experimental results on two benchmark gait databases, CASIA-B and OU-ISIR, demonstrate the effectiveness of the proposed method. Extensive experiments also show that our MMMSL achieves significant improvements compared with related subspace learning based methods.