Improved Model-Free Gait Recognition Based on Human Body Part

Gait recognition aims to identify people through the analysis of the way they walk. The challenge of model-free based gait recognition is to cope with various intra-class variations such as clothing variations, carrying conditions, and angle variations that adversely affect the recognition performance. This chapter proposes a method to select the most discriminative human body part based on group Lasso of motion to reduce the intra-class variation so as to improve the recognition performance. The proposed method is evaluated using CASIA gait database (dataset B), and the experimental results suggest that our method yields 88.75 % of Correct Classification Rate (CCR) when compared to existing state-of-the-art methods.

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