D
2016
Learning Robust Features for Gait Recognition by Maximum Margin Criterion
BALÁŽIA, Michal and Petr SOJKA
Basic information
Original name
Learning Robust Features for Gait Recognition by Maximum Margin Criterion
Authors
BALÁŽIA, Michal (703 Slovakia, guarantor, belonging to the institution) and Petr SOJKA (203 Czech Republic, belonging to the institution)
Edition
USA, Proceedings of the 23rd IEEE/IAPR International Conference on Pattern Recognition (ICPR 2016), p. 901-906, 6 pp. 2016
Other information
Type of outcome
Stať ve sborníku
Field of Study
Informatics
Country of publisher
United States of America
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
electronic version available online
RIV identification code
RIV/00216224:14330/16:00090367
Organization
Fakulta informatiky – Repository – Repository
Keywords (in Czech)
rozpoznávání podle chůze
Keywords in English
gait recognition
Links
MUNI/A/0892/2015, interní kód Repo. MUNI/A/0935/2015, interní kód Repo.
V originále
In the field of gait recognition from motion capture data, designing human-interpretable gait features is a common practice of many fellow researchers. To refrain from ad-hoc schemes and to find maximally discriminative features we may need to explore beyond the limits of human interpretability. This paper contributes to the state-of-the-art with a machine learning approach for extracting robust gait features directly from raw joint coordinates. The features are learned by a modification of Linear Discriminant Analysis with Maximum Margin Criterion so that the identities are maximally separated and, in combination with an appropriate classifier, used for gait recognition. Experiments on the CMU MoCap database show that this method outperforms eight other relevant methods in terms of the distribution of biometric templates in respective feature spaces expressed in four class separability coefficients. Additional experiments indicate that this method is a leading concept for rank-based classifier systems.
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