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