Cross view gait recognition by metric learning

In this paper, we propose the human recognition framework based on the biometric trait conveyed by a walking subject, where the viewing angles of gallery and probe may differ. To deal with this kind of intra-class variance, we propose to exploit the view transformation technology to transform the em...

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Bibliographic Details
Published in2014 IEEE International Conference on Consumer Electronics - Taiwan pp. 81 - 82
Main Authors Chun-Chieh Lee, Chi-Hung Chuang, Fanzi Wu, Luo-Wei Tsai, Kuo-Chin Fan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2014
Subjects
Gait recognition
Manifolds
Matrix decomposition
Measurement
Probes
Training
Vectors
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Summary:In this paper, we propose the human recognition framework based on the biometric trait conveyed by a walking subject, where the viewing angles of gallery and probe may differ. To deal with this kind of intra-class variance, we propose to exploit the view transformation technology to transform the embedded vector of one viewing angle into another embedded vector of target viewing angle. Then, the metric already learned previously on target manifold will be use to measure the similarity between vectors. Experiments were conducted on CASIA-B gait database and the results demonstrate the notable improvement of cross view gait recognition performance via the combination of feature transformation and metric learning.
DOI:10.1109/ICCE-TW.2014.6904112