Robust view transformation model for gait recognition
Recent gait recognition systems often suffer from the challenges including viewing angle variation and large intra-class variations. In order to address these challenges, this paper presents a robust View Transformation Model for gait recognition. Based on the gait energy image, the proposed method...
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| creator | Shuai Zheng Junge Zhang Kaiqi Huang Ran He Tieniu Tan |
| description | Recent gait recognition systems often suffer from the challenges including viewing angle variation and large intra-class variations. In order to address these challenges, this paper presents a robust View Transformation Model for gait recognition. Based on the gait energy image, the proposed method establishes a robust view transformation model via robust principal component analysis. Partial least square is used as feature selection method. Compared with the existing methods, the proposed method finds out a shared linear correlated low rank subspace, which brings the advantages that the view transformation model is robust to viewing angle variation, clothing and carrying condition changes. Conducted on the CASIA gait dataset, experimental results show that the proposed method outperforms the other existing methods. |
| doi_str_mv | 10.1109/ICIP.2011.6115889 |
| format | Conference Proceeding |
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In order to address these challenges, this paper presents a robust View Transformation Model for gait recognition. Based on the gait energy image, the proposed method establishes a robust view transformation model via robust principal component analysis. Partial least square is used as feature selection method. Compared with the existing methods, the proposed method finds out a shared linear correlated low rank subspace, which brings the advantages that the view transformation model is robust to viewing angle variation, clothing and carrying condition changes. 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In order to address these challenges, this paper presents a robust View Transformation Model for gait recognition. Based on the gait energy image, the proposed method establishes a robust view transformation model via robust principal component analysis. Partial least square is used as feature selection method. Compared with the existing methods, the proposed method finds out a shared linear correlated low rank subspace, which brings the advantages that the view transformation model is robust to viewing angle variation, clothing and carrying condition changes. Conducted on the CASIA gait dataset, experimental results show that the proposed method outperforms the other existing methods.</abstract><pub>IEEE</pub><doi>10.1109/ICIP.2011.6115889</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 1522-4880 |
| ispartof | 2011 18th IEEE International Conference on Image Processing, 2011, p.2073-2076 |
| issn | 1522-4880 2381-8549 |
| language | eng |
| recordid | cdi_ieee_primary_6115889 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Conferences Feature extraction Gait Recognition Image recognition Legged locomotion Low-rank Probes Robustness Vectors View Transformation Model |
| title | Robust view transformation model for gait recognition |
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