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Combining gait and face for tackling the elapsed time challenges
conference contribution
posted on 2013-01-01, 00:00 authored by Y Guan, X Wei, Chang-Tsun LiChang-Tsun Li, G L Marcialis, F Roli, M TistarelliRandom Subspace Method (RSM) has been demonstrated as an effective framework for gait recognition. Through combining a large number of weak classifiers, the generalization errors can be greatly reduced. Although RSM-based gait recognition system is robust to a large number of covariate factors, it is, in essence an unimodal biometric system and has the limitations when facing extremely large intra-class variations. One of the major challenges is the elapsed time covariate, which may affect the human walking style in an unpredictable manner. To tackle this challenge, in this paper we propose a multimodal-RSM framework, and side face is used to strengthen the weak classifiers without compromising the generalization power of the whole system. We evaluate our method on the TUM-GAID dataset, and it significantly outperforms other multimodal methods. Specifically, our method achieves very competitive results for tackling the most challenging elapsed time covariate, which potentially also includes the changes in shoe, carrying status, clothing, lighting condition, etc.
History
Event
IEEE Biometrics Council. Conference (6th : 2013 : Washington, D.C.)Series
IEEE Biometrics Council ConferencePagination
1 - 8Publisher
Institute of Electrical and Electronics EngineersLocation
Washington, D.C.Place of publication
Piscataway, N.J.Publisher DOI
Start date
2013-09-29End date
2013-10-02ISBN-13
9781479905270Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2013, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
BTAS 2013 : Proceedings of the 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and SystemsUsage metrics
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