Learning Robust Features for Gait Recognition by Maximum Margin
Criterion

D 2016

Learning Robust Features for Gait Recognition by Maximum Margin Criterion

BALÁŽIA, Michal a Petr SOJKA

Základní údaje

Originální název

Learning Robust Features for Gait Recognition by Maximum Margin Criterion

Autoři

BALÁŽIA, Michal (703 Slovensko, garant, domácí) a Petr SOJKA (203 Česká republika, domácí)

Vydání

USA, Proceedings of the 23rd IEEE/IAPR International Conference on Pattern Recognition (ICPR 2016), od s. 901-906, 6 s. 2016

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

DOI conference web Proceedings

Kód RIV

RIV/00216224:14330/16:00090367

Organizační jednotka

Fakulta informatiky

ISBN

978-1-5090-4847-2

DOI

http://dx.doi.org/10.1109/ICPR.2016.7899750

UT WoS

000406771300153

Klíčová slova česky

rozpoznávání podle chůze

Klíčová slova anglicky

gait recognition

Štítky

best4, core_B, firank_A, gait, gait recognition, ICPR

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 4. 4. 2018 17:04, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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. Additional experiments indicate that this method is a leading concept for rank-based classifier systems.

Návaznosti

MUNI/A/0892/2015, interní kód MU

Název: Výzkum v aplikované informatice na FI MU (Akronym: VAIFIMU)

Investor: Masarykova univerzita, Výzkum v aplikované informatice na FI MU, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

MUNI/A/0935/2015, interní kód MU

Název: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity (Akronym: SKOMU)

Investor: Masarykova univerzita, Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Přiložené soubory

icpr679arxiv.pdf

Vyhledat podobné dokumenty

Citovat

BALÁŽIA, Michal a Petr SOJKA. Learning Robust Features for Gait Recognition by Maximum Margin Criterion. Online. In Eduardo Bayro-Corrochano, Gabrielle Sanniti di Baja, Gérard Medioni. Proceedings of the 23rd IEEE/IAPR International Conference on Pattern Recognition (ICPR 2016). USA: IEEE, 2016, s. 901-906. ISBN 978-1-5090-4847-2. Dostupné z: https://dx.doi.org/10.1109/ICPR.2016.7899750.

@inproceedings{1349591,
   author = {Balážia, Michal and Sojka, Petr},
   address = {USA},
   booktitle = {Proceedings of the 23rd IEEE/IAPR International Conference on Pattern Recognition (ICPR 2016)},
   doi = {http://dx.doi.org/10.1109/ICPR.2016.7899750},
   editor = {Eduardo Bayro-Corrochano, Gabrielle Sanniti di Baja, Gérard Medioni},
   keywords = {gait recognition},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {USA},
   isbn = {978-1-5090-4847-2},
   pages = {901-906},
   publisher = {IEEE},
   title = {Learning Robust Features for Gait Recognition by Maximum Margin Criterion},
   url = {https://doi.org/10.1109/ICPR.2016.7899750},
   year = {2016}
}

TY  - JOUR
ID  - 1349591
AU  - Balážia, Michal - Sojka, Petr
PY  - 2016
TI  - Learning Robust Features for Gait Recognition by Maximum Margin Criterion
PB  - IEEE
CY  - USA
SN  - 9781509048472
KW  - gait recognition
UR  - https://doi.org/10.1109/ICPR.2016.7899750
L2  - https://doi.org/10.1109/ICPR.2016.7899750
N2  - 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. Additional experiments indicate that this method is a leading concept for rank-based classifier systems.
ER  -

BALÁŽIA, Michal a Petr SOJKA. Learning Robust Features for Gait Recognition by Maximum Margin Criterion. Online. In Eduardo Bayro-Corrochano, Gabrielle Sanniti di Baja, Gérard Medioni. \textit{Proceedings of the 23rd IEEE/IAPR International Conference on Pattern Recognition (ICPR 2016)}. USA: IEEE, 2016, s.~901-906. ISBN~978-1-5090-4847-2. Dostupné z: https://dx.doi.org/10.1109/ICPR.2016.7899750.

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