Abstract
Human gait represents an attractive biometric modality to re-identify a person as it requires non contact and it is perceivable at a distance. However, the view angle variation and the presence of covariate factors cause significant difficulties for recognizing gaits. In order to deal with such constraints, this paper presents a Part View Transformation Model (PVTM) for gait based applications. Compared with previous methods, the PVTM is applied on selected relevant parts chosen through a semantic classification step. Conducted on the CASIA-B gait database, experimental results show that the proposed method outperforms well known multi-view methods even under covariate factors (i.e. carrying bag, clothing).
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