Improved human gait recognition

Abstract

Gait recognition is an emerging biometric technology which aims to identify people purely through the analysis of the way they walk. The technology has attracted interest as a method of identification because of its non-invasiveness, since it does not require the subject’s cooperation. However, “covariates” which include clothing, carrying conditions, and other intra-class variations affect the recognition performances. This paper proposes a feature selection mask which is able to select most relevant discriminative features for human recognition to alleviate the impact of covariates so as to improve the recognition performances. The proposed method has been evaluated using CASIA Gait Database (Dataset B) and the experimental results demonstrate that the proposed technique yields 77.38 % of correct recognition.