Interpretable Gait Recognition by Granger Causality
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F22%3A00125672" target="_blank" >RIV/00216224:14330/22:00125672 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICPR56361.2022.9956624" target="_blank" >https://doi.org/10.1109/ICPR56361.2022.9956624</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICPR56361.2022.9956624" target="_blank" >10.1109/ICPR56361.2022.9956624</a>
Alternative languages
Result language
angličtina
Original language name
Interpretable Gait Recognition by Granger Causality
Original language description
Which joint interactions in the human gait cycle can be used as biometric characteristics? Most current methods on gait recognition suffer from a lack of interpretability. We propose an interpretable feature representation of gait sequences by the graphical Granger causal inference. The gait sequence of a person in the standardized motion capture format, constituting a set of 3D joint spatial trajectories, is envisaged as a causal system of joints interacting in time. We apply the graphical Granger model (GGM) to obtain the so-called Granger causal graph among joints as a discriminative and visually interpretable representation of a person's gait. We evaluate eleven distance functions in the GGM feature space by established classification and class-separability evaluation metrics. Our experiments indicate that, depending on the metric, the most appropriate distance functions for the GGM are the total norm distance and the Ky-Fan 1-norm distance. Experiments also show that the GGM is able to detect the most discriminative joint interactions and that it outperforms five related interpretable models in correct classification rate and in the Davies-Bouldin index. The proposed GGM model can serve as a complementary tool for gait analysis in kinesiology or for gait recognition in video surveillance.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of 26th International Conference on Pattern Recognition, ICPR 2022
ISBN
9781665490627
ISSN
1051-4651
e-ISSN
—
Number of pages
7
Pages from-to
1069-1075
Publisher name
IEEE
Place of publication
Los Alamitos, CA, USA
Event location
Montréal, Québec, Canada
Event date
Aug 21, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000897707601011