BALÁŽIA, Michal and Petr SOJKA. Gait Recognition from Motion Capture Data. ACM Transactions on Multimedia Computing, Communications and Applications (TOMM), special issue on Representation, Analysis and Recognition of 3D Humans. New York, USA: ACM, 2018, vol. 14, 1s, p. 1-18. ISSN 1551-6857. Available from: https://dx.doi.org/10.1145/3152124. |
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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, 2018, vol.~14, 1s, p.~1-18. ISSN~1551-6857. Available from: https://dx.doi.org/10.1145/3152124.
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