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Recognizing Gaits Across Views Through Correlated Motion Co-Clustering
Kusakunniran, Worapan1; Wu, Qiang2; Zhang, Jian3,4; Li, Hongdong5,6; Wang, Liang7
AbstractHuman gait is an important biometric feature, which can be used to identify a person remotely. However, view change can cause significant difficulties for gait recognition because it will alter available visual features for matching substantially. Moreover, it is observed that different parts of gait will be affected differently by view change. By exploring relations between two gaits from two different views, it is also observed that a part of gait in one view is more related to a typical part than any other parts of gait in another view. A new method proposed in this paper considers such variance of correlations between gaits across views that is not explicitly analyzed in the other existing methods. In our method, a novel motion co-clustering is carried out to partition the most related parts of gaits from different views into the same group. In this way, relationships between gaits from different views will be more precisely described based on multiple groups of the motion co-clustering instead of a single correlation descriptor. Inside each group, a linear correlation between gait information across views is further maximized through canonical correlation analysis (CCA). Consequently, gait information in one view can be projected onto another view through a linear approximation under the trained CCA subspaces. In the end, a similarity between gaits originally recorded from different views can be measured under the approximately same view. Comprehensive experiments based on widely adopted gait databases have shown that our method outperforms the state-of-the-art.
KeywordGait Recognition Human Identification View Change Co-clustering Bipartite Graph Multipartitioning Canonical Correlation Analysis
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000329581800017
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Document Type期刊论文
Affiliation1.Mahidol Univ, Fac Informat & Commun Technol, Nakhon Pathom 73170, Thailand
2.Univ Technol Sydney, Sch Comp & Commun, Sydney, NSW 2007, Australia
3.Univ Technol Sydney, Fac Engn & Informat Technol, Sch Software, Adv Analyt Inst, Sydney, NSW 2007, Australia
4.Natl ICT Australia, Neville Roach Lab, Kensington, NSW 2052, Australia
5.Australian Natl Univ, Res Sch Informat Sci & Engn, Canberra, ACT 0200, Australia
6.Natl ICT Australia, Canberra Res Lab, Canberra, ACT 2601, Australia
7.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Kusakunniran, Worapan,Wu, Qiang,Zhang, Jian,et al. Recognizing Gaits Across Views Through Correlated Motion Co-Clustering[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2014,23(2):696-709.
APA Kusakunniran, Worapan,Wu, Qiang,Zhang, Jian,Li, Hongdong,&Wang, Liang.(2014).Recognizing Gaits Across Views Through Correlated Motion Co-Clustering.IEEE TRANSACTIONS ON IMAGE PROCESSING,23(2),696-709.
MLA Kusakunniran, Worapan,et al."Recognizing Gaits Across Views Through Correlated Motion Co-Clustering".IEEE TRANSACTIONS ON IMAGE PROCESSING 23.2(2014):696-709.
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