Další formáty:
BibTeX
LaTeX
RIS
@inproceedings{23246,
author = {Balážia, Michal and Sojka, Petr},
address = {USA},
booktitle = {Proceedings of the 23rd IEEE/IAPR International Conference on Pattern Recognition (ICPR 2016)},
keywords = {gait recognition},
howpublished = {elektronická verze "online"},
language = {eng},
location = {USA},
isbn = {978-1-5090-4847-2},
pages = {901-906},
publisher = {IEEE},
title = {Learning Robust Features for Gait Recognition by Maximum Margin Criterion},
url = {http://ieeexplore.ieee.org/servlet/opac?punumber=1000545},
year = {2016}
}
TY - JOUR
ID - 23246
AU - Balážia, Michal - Sojka, Petr
PY - 2016
TI - Learning Robust Features for Gait Recognition by Maximum Margin Criterion
PB - IEEE
CY - USA
SN - 9781509048472
KW - gait recognition
UR - http://ieeexplore.ieee.org/servlet/opac?punumber=1000545
N2 - 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.
ER -
BALÁŽIA, Michal a Petr SOJKA. Learning Robust Features for Gait Recognition by Maximum Margin Criterion. In \textit{Proceedings of the 23rd IEEE/IAPR International Conference on Pattern Recognition (ICPR 2016)}. USA: IEEE. s.~901-906. ISBN~978-1-5090-4847-2. 2016.
|
