Part-based human gait identification under clothing and carrying condition variations

Gait recognition has already achieved satisfactory performance on small databases under ideal conditions. Most of the existing approaches represent gait pattern using a locomotion model or statistic model of human silhouette. However, it is still a challenging task to conduct human gait identificati...

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Veröffentlicht in:2010 International Conference on Machine Learning and Cybernetics Jg. 1; S. 268 - 273
Hauptverfasser: Ning Li, Yi Xu, Xiao-Kang Yang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2010
Schlagworte:
Carrying condition
Feature selection
Gait identification
Humans
Legged locomotion
Machine learning
Pixel
Probes
ISBN:9781424465262, 1424465265
ISSN:2160-133X
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Zusammenfassung:Gait recognition has already achieved satisfactory performance on small databases under ideal conditions. Most of the existing approaches represent gait pattern using a locomotion model or statistic model of human silhouette. However, it is still a challenging task to conduct human gait identification under variations of clothing and carrying condition in real scenes. In this paper, an adaptive part-based feature selection method is proposed to filter out interference feature blocks and a matching procedure is performed to identify the correct subject. Compared with the state-of-the-art methods on a large standard dataset, the proposed method shows an encouraging computational complexity reduction and performance improvement in identification rates.
ISBN:9781424465262
1424465265
ISSN:2160-133X
DOI:10.1109/ICMLC.2010.5581055