Walker-Independent Features for Gait Recognition from Motion Capture Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F16%3A00090768" target="_blank" >RIV/00216224:14330/16:00090768 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-319-49055-7_28" target="_blank" >https://doi.org/10.1007/978-3-319-49055-7_28</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-49055-7_28" target="_blank" >10.1007/978-3-319-49055-7_28</a>
Alternative languages
Result language
angličtina
Original language name
Walker-Independent Features for Gait Recognition from Motion Capture Data
Original language description
MoCap-based human identification, as a pattern recognition discipline, can be optimized using a machine learning approach. Yet in some applications such as video surveillance new identities can appear on the fly and labeled data for all encountered people may not always be available. This work introduces the concept of learning walker-independent gait features directly from raw joint coordinates by a modification of the Fisher’s Linear Discriminant Analysis with Maximum Margin Criterion. Our new approach shows not only that these features can discriminate different people than who they are learned on, but also that the number of learning identities can be much smaller than the number of walkers encountered in the real operation.
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 joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2016) and Statistical Techniques in Pattern Recognition (SPR 2016)
ISBN
9783319490540
ISSN
0302-9743
e-ISSN
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Number of pages
12
Pages from-to
310-321
Publisher name
Springer International Publishing AG
Place of publication
Switzerland
Event location
Mérida, Mexico
Event date
Jan 1, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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