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Towards Human Body-Part Learning for Model-Free Gait Recognition

2019-04-02 18:49:14
Imad Rida

Abstract

Gait based biometric aims to discriminate among people by the way or manner they walk. It represents a biometric at distance which has many advantages over other biometric modalities. State-of-the-art methods require a limited cooperation from the individuals. Consequently, contrary to other modalities, gait is a non-invasive approach. As a behavioral analysis, gait is difficult to circumvent. Moreover, gait can be performed without the subject being aware of it. Consequently, it is more difficult to try to tamper one own biometric signature. In this paper we review different features and approaches used in gait recognition. A novel method able to learn the discriminative human body-parts to improve the recognition accuracy will be introduced. Extensive experiments will be performed on CASIA gait benchmark database and results will be compared to state-of-the-art methods.

Abstract (translated)

基于步态的生物识别旨在通过人们走路的方式或方式来区分他们。它是一种远程生物识别技术,与其他生物识别技术相比具有许多优势。最先进的方法需要个人的有限合作。因此,与其他方式相反,步态是一种非侵入性方法。作为一种行为分析,步态很难规避。此外,步态可以在没有被试意识到的情况下进行。因此,更难尝试篡改自己的生物特征签名。本文综述了步态识别的不同特点和方法。介绍了一种新的学习人体识别部位的方法,以提高识别精度。将在卡西娅步态基准数据库上进行大量实验,并将结果与最先进的方法进行比较。

URL

https://arxiv.org/abs/1904.01620

PDF

https://arxiv.org/pdf/1904.01620.pdf


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