Learning Robust Features for Gait Recognition by Maximum Margin Criterion

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning Robust Features for Gait Recognition by Maximum Margin Criterion

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Learning Robust Features for Gait Recognition by Maximum Margin Criterion

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 23rd IEEE/IAPR International Conference on Pattern Recognition (ICPR 2016)

ISBN

9781509048472

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

901-906

Název nakladatele

IEEE

Místo vydání

USA

Místo konání akce

Cancun, Mexico

Datum konání akce

1. 1. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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