Uncooperative gait recognition: Re-ranking based on sparse coding and multi-view hypergraph learning

Gait is an important biometric which can operate from a distance without subject cooperation. However, it is easily affected by changes in covariate conditions (carrying, clothing, view angle, walking speed, random noise etc.). It is hard for training set to cover all conditions. Bipartite ranking m...

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Bibliographic Details
Published in:Pattern recognition Vol. 53; pp. 116 - 129
Main Authors: Chen, Xin, Xu, Jiaming
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.05.2016
Subjects:
Biometrics
Coding
Cooperation
Gait recognition
Hypergraph learning
Learning
Pattern recognition
Ranking
Re-ranking
Recognition
Sparse Coding
Uncooperative gait recognition
Uncooperative gait recognition
Sparse Coding
Hypergraph learning
Re-ranking
ISSN:0031-3203, 1873-5142
Online Access:Get full text
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Summary:Gait is an important biometric which can operate from a distance without subject cooperation. However, it is easily affected by changes in covariate conditions (carrying, clothing, view angle, walking speed, random noise etc.). It is hard for training set to cover all conditions. Bipartite ranking model has achieved success in gait recognition without assumption of subject cooperation. We propose a multi-view hypergraph learning re-ranking (MHLRR) method by integrating multi-view hypergraph learning (MHL) with hypergraph-based re-ranking framework. Sparse coding re-ranking (SCRR) and MHLRR are integrated under the graph-based framework to get a model. We define it as the sparse coding multi-view hypergraph learning re-ranking (SCMHLRR) method, which makes our approach achieve higher recognition accuracy under a genuine uncooperative setting. Extensive experiments demonstrate that our approach drastically outperforms existing ranking based methods, achieving good increase in recognition rate under the most difficult uncooperative settings.
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ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2015.11.016