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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| Published in: | Pattern recognition Vol. 53; pp. 116 - 129 |
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| Language: | English |
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01.05.2016
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| ISSN: | 0031-3203, 1873-5142 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Xu, Jiaming Chen, Xin |
| Author_xml | – sequence: 1 givenname: Xin surname: Chen fullname: Chen, Xin email: chenxin@stu.jnu.edu.cn organization: Department of Information Science and Technology, Jinan University, Guangzhou, China – sequence: 2 givenname: Jiaming surname: Xu fullname: Xu, Jiaming email: 19910411xjm@sina.com organization: Baiyun District Bureau of Justice, Guangzhou, China |
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| Keywords | Uncooperative gait recognition Sparse Coding Hypergraph learning Re-ranking |
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