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, vol. 14, 1s, p. 1-18. ISSN 1551-6857. 2018.
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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 Informatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
WWW URL URL
RIV identification code RIV/00216224:14330/18:00102051
Organization Fakulta informatiky – Repository – Repository
UT WoS 000433517100008
Keywords (in Czech) rozpoznávání podle chůze
Keywords in English gait recognition
Links MUNI/A/0992/2016, interní kód Repo. MUNI/A/0997/2016, interní kód Repo.
Changed by Changed by: RNDr. Daniel Jakubík, učo 139797. Changed: 1/10/2020 01:51.
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.
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