Learning Robust Features for Gait Recognition by Maximum Margin Criterion
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F16%3A00090367" target="_blank" >RIV/00216224:14330/16:00090367 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICPR.2016.7899750" target="_blank" >https://doi.org/10.1109/ICPR.2016.7899750</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICPR.2016.7899750" target="_blank" >10.1109/ICPR.2016.7899750</a>
Alternative languages
Result language
angličtina
Original language name
Learning Robust Features for Gait Recognition by Maximum Margin Criterion
Original language description
In the field of gait recognition from motion capture data, designing human-interpretable gait features is a common practice of many fellow researchers. To refrain from ad-hoc schemes and to find maximally discriminative features we may need to explore beyond the limits of human interpretability. This paper contributes to the state-of-the-art with a machine learning approach for extracting robust gait features directly from raw joint coordinates. The features are learned by a modification of Linear Discriminant Analysis with Maximum Margin Criterion so that the identities are maximally separated and, in combination with an appropriate classifier, used for gait recognition. Experiments on the CMU MoCap database show that this method outperforms eight other relevant methods in terms of the distribution of biometric templates in respective feature spaces expressed in four class separability coefficients.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
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 the 23rd IEEE/IAPR International Conference on Pattern Recognition (ICPR 2016)
ISBN
9781509048472
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
901-906
Publisher name
IEEE
Place of publication
USA
Event location
Cancun, Mexico
Event date
Jan 1, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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