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@article{1390685,
author = {Balážia, Michal and Sojka, Petr},
article_location = {New York, USA},
article_number = {1s},
doi = {http://dx.doi.org/10.1145/3152124},
keywords = {gait recognition},
language = {eng},
issn = {1551-6857},
journal = {ACM Transactions on Multimedia Computing, Communications and Applications (TOMM), special issue on Representation, Analysis and Recognition of 3D Humans},
note = {ACM TOMM},
title = {Gait Recognition from Motion Capture Data},
url = {https://doi.org/10.1145/3152124},
volume = {14},
year = {2018}
}
TY - JOUR
ID - 1390685
AU - Balážia, Michal - Sojka, Petr
PY - 2018
TI - Gait Recognition from Motion Capture Data
JF - ACM Transactions on Multimedia Computing, Communications and Applications (TOMM), special issue on Representation, Analysis and Recognition of 3D Humans
VL - 14
IS - 1s
SP - 1-18
EP - 1-18
PB - ACM
SN - 15516857
N1 - ACM TOMM
KW - gait recognition
UR - https://doi.org/10.1145/3152124
L2 - https://doi.org/10.1145/3152124
N2 - Gait recognition from motion capture data, as a pattern classification discipline, can be improved by the use of machine learning. This paper contributes to the state-of-the-art with a statistical approach for extracting robust gait features directly from raw data by a modification of Linear Discriminant Analysis with Maximum Margin Criterion. Experiments on the CMU MoCap database show that the suggested method outperforms thirteen relevant methods based on geometric features and a method to learn the features by a combination of Principal Component Analysis and Linear Discriminant Analysis. The methods are evaluated in terms of the distribution of biometric templates in respective feature spaces expressed in a number of class separability coefficients and classification metrics. Results also indicate a high portability of learned features, that means, we can learn what aspects of walk people generally differ in and extract those as general gait features. Recognizing people without needing group-specific features is convenient as particular people might not always provide annotated learning data. As a contribution to reproducible research, our evaluation framework and database have been made publicly available. This research makes motion capture technology directly applicable for human recognition.
ER -
BALÁŽIA, Michal and Petr SOJKA. Gait Recognition from Motion Capture Data. \textit{ACM Transactions on Multimedia Computing, Communications and Applications (TOMM), special issue on Representation, Analysis and Recognition of 3D Humans}. New York, USA: ACM, vol.~14, 1s, p.~1-18. ISSN~1551-6857. doi:10.1145/3152124. 2018.
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