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. doi:10.1145/3152124.
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Basic information
Original name Gait Recognition from Motion Capture Data
Authors BALÁŽIA, Michal (703 Slovakia, guarantor, belonging to the institution) and Petr SOJKA (203 Czech Republic, belonging to the institution).
Edition ACM Transactions on Multimedia Computing, Communications and Applications (TOMM), special issue on Representation, Analysis and Recognition of 3D Humans, New York, USA, ACM, 2018, 1551-6857.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
WWW DOI software, developed framework and database for reproducibility
Impact factor Impact factor: 2.870
RIV identification code RIV/00216224:14330/18:00102051
Organization unit Faculty of Informatics
Doi http://dx.doi.org/10.1145/3152124
UT WoS 000433517100008
Keywords (in Czech) rozpoznávání podle chůze
Keywords in English gait recognition
Tags best2, gait recognition
Tags International impact, Reviewed
Changed by Changed by: doc. RNDr. Petr Sojka, Ph.D., učo 2378. Changed: 30/9/2020 23:11.
Abstract
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.
Links
MUNI/A/0992/2016, internal MU codeName: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity (Acronym: SKOMU)
Investor: Masaryk University, Grant Agency of Masaryk University, Category A
MUNI/A/0997/2016, internal MU codeName: Aplikovaný výzkum na FI: vyhledávacích systémy, bezpečnost, vizualizace dat a virtuální realita.
Investor: Masaryk University, Grant Agency of Masaryk University, Category A
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