@inproceedings{b60936962efd4d3ba017e37374897d19,
title = "Cross view gait recognition by metric learning",
abstract = "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.",
author = "Lee, {Chun Chieh} and Chuang, {Chi Hung} and Fanzi Wu and Tsai, {Luo Wei} and Fan, {Kuo Chin}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 1st IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2014 ; Conference date: 26-05-2014 Through 28-05-2014",
year = "2014",
month = sep,
day = "18",
doi = "10.1109/ICCE-TW.2014.6904112",
language = "???core.languages.en_GB???",
series = "Digest of Technical Papers - IEEE International Conference on Consumer Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "81--82",
booktitle = "Digest of Technical Papers - IEEE International Conference on Consumer Electronics",
}