标题:A general tensor representation framework for cross-view gait recognition
作者:Ben, Xianye; Zhang, Peng; Lai, Zhihui; Yan, Rui; Zhai, Xinliang; Meng, Weixiao
作者机构:[Ben, Xianye; Zhang, Peng; Zhai, Xinliang] Shandong Univ, Sch Informat Sci & Engn, Qingdao 266237, Shandong, Peoples R China.; [Zhang, Peng] Univ Te 更多
通讯作者:Ben, Xianye;Ben, XY
通讯作者地址:[Ben, XY]Shandong Univ, Sch Informat Sci & Engn, Qingdao 266237, Shandong, Peoples R China.
来源:PATTERN RECOGNITION
出版年:2019
卷:90
页码:87-98
DOI:10.1016/j.patcog.2019.01.017
关键词:Gait recognition; Cross-view gait; Tensor representation; Framework
摘要: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-theart cross-view gait recognition approaches. (C) 2019 Elsevier Ltd. All rights reserved.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060329613&doi=10.1016%2fj.patcog.2019.01.017&partnerID=40&md5=7ca29d47c65308ae2a40ff3d325a83af
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