A general tensor representation framework for cross-view gait recognition. (June 2019)

• Record Type: Journal Article
• Authors: Ben, Xianye; Zhang, Peng; Lai, Zhihui; Yan, Rui; Zhai, Xinliang; Meng, Weixiao
• Is Part Of: Pattern recognition. Volume 90(2019:Jun.)
• Page Start: 87
• Page End: 98
• Publication Date: 2019-06
• Subjects: Gait recognition -- Cross-view gait -- Tensor representation -- Framework; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.01.017
• Languages: English
• ISSNs: 0031-3203

Abstract

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.