A general tensor representation framework for cross-view gait recognition

Abstract

Tensor analysis methods have played an important role in identifying human gaits using high dimensional data. However, when view angles change, it becomes more and more difficult to recognize cross-view gait by learning only a set of multi-linear projection matrices. To address this problem, a general tensor representation framework for cross-view gait recognition is proposed in this paper. There are three criteria of tensorial coupled mappings in the proposed framework. (1) Coupled multi-linear locality-preserved criterion (CMLP) aims to detect the essential tensorial manifold structure via preserving local information. (2) Coupled multi-linear marginal fisher criterion (CMMF) aims to encode the intra-class compactness and inter-class separability with local relationships. (3) Coupled multi-linear discriminant analysis criterion (CMDA) aims to minimize the intra-class scatter and maximize the inter-class scatter. For the three tensor algorithms for cross-view gaits, two sets of multi-linear projection matrices are iteratively learned using alternating projection optimization procedures. The proposed methods are compared with the recently published cross-view gait recognition approaches on CASIA(B) and OU-ISIR gait database. The results demonstrate that the performances of the proposed methods are superior to existing state-of-the-art cross-view gait recognition approaches.