Gait recognition using gait entropy image
Gait as a behavioural biometric is concerned with how people walk. However, most existing gait representations capture both motion and appearance information. They are thus sensitive to changes in various covariate conditions such as carrying and clothing. In this paper, a novel gait representation...
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| Published in: | 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009) p. P2 |
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| Main Authors: | , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
Stevenage
IET
2009
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| ISBN: | 1849192073, 9781849192071 |
| Online Access: | Get full text |
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| Abstract | Gait as a behavioural biometric is concerned with how people walk. However, most existing gait representations capture both motion and appearance information. They are thus sensitive to changes in various covariate conditions such as carrying and clothing. In this paper, a novel gait representation termed as Gait Entropy Image (GEnI) is proposed. Based on computing entropy, a GEnI encodes in a single image the randomness of pixel values in the silhouette images over a complete gait cycle. It thus captures mostly motion information and is robust to covariate condition changes that affect appearance. Extensive experiments on the USF HumanID dataset, CASIA dataset and the SOTON dataset have been carried out to demonstrate that the proposed gait representation outperforms existing methods, especially when there are significant appearance changes. Our experiments also show clear advantage of GEnI over the alternatives without the assumption on cooperative subjects, i.e. both the gallery and the probe sets consist of a mixture of gait sequences under different and unknown covariate conditions. (6 pages) |
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| AbstractList | Gait as a behavioural biometric is concerned with how people walk. However, most existing gait representations capture both motion and appearance information. They are thus sensitive to changes in various covariate conditions such as carrying and clothing. In this paper, a novel gait representation termed as Gait Entropy Image (GEnI) is proposed. Based on computing entropy, a GEnI encodes in a single image the randomness of pixel values in the silhouette images over a complete gait cycle. It thus captures mostly motion information and is robust to covariate condition changes that affect appearance. Extensive experiments on the USF HumanID dataset, CASIA dataset and the SOTON dataset have been carried out to demonstrate that the proposed gait representation outperforms existing methods, especially when there are significant appearance changes. Our experiments also show clear advantage of GEnI over the alternatives without the assumption on cooperative subjects, i.e. both the gallery and the probe sets consist of a mixture of gait sequences under different and unknown covariate conditions. (6 pages) |
| Author | Tao Xiang Bashir, K Shaogang Gong |
| Author_xml | – sequence: 1 givenname: K surname: Bashir fullname: Bashir, K organization: Sch. of Electron. Eng. & Comput. Sci., Queen Mary Univ. of London, London – sequence: 2 surname: Tao Xiang fullname: Tao Xiang – sequence: 3 surname: Shaogang Gong fullname: Shaogang Gong |
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| DOI | 10.1049/ic.2009.0230 |
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| Keywords | behavioural biometric gait recognition entropy image representation SOTON dataset gait entropy image silhouette images biomimetics gait analysis gait representation motion information |
| Language | English |
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| Snippet | Gait as a behavioural biometric is concerned with how people walk. However, most existing gait representations capture both motion and appearance information.... |
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| SubjectTerms | Computer vision and image processing techniques Optical, image and video signal processing |
| Title | Gait recognition using gait entropy image |
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