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Combining gait and face for tackling the elapsed time challenges

Version 2 2024-06-05, 03:28
Version 1 2019-06-27, 15:02
conference contribution
posted on 2024-06-05, 03:28 authored by Y Guan, X Wei, Chang-Tsun LiChang-Tsun Li, GL Marcialis, F Roli, M Tistarelli
Random 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

Pagination

1-8

Location

Washington, D.C.

Start date

2013-09-29

End date

2013-10-02

ISBN-13

9781479905270

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2013, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

BTAS 2013 : Proceedings of the 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems

Event

IEEE Biometrics Council. Conference (6th : 2013 : Washington, D.C.)

Publisher

Institute of Electrical and Electronics Engineers

Place of publication

Piscataway, N.J.

Series

IEEE Biometrics Council Conference

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