Abstract
Direction changes cause difficulties for most of the gait recognition systems, due to appearance changes. We propose a new approach for multi-view gait recognition, which focuses on recognizing people walking on unconstrained (curved and straight) paths. To this effect, we present a new rotation invariant gait descriptor which is based on 3D angular analysis of the movement of the subject. Our method does not require the sequence to be split into gait cycles, and is able to provide a response before processing the whole sequence. A Support Vector Machine is used for classifying, and a sliding temporal window with majority vote policy is used to reinforce the classification results. The proposed approach has been experimentally validated on “AVA Multi-View Dataset” and “Kyushu University 4D Gait Database” and compared with related state-of-art work. Experimental results demonstrate the effectiveness of this approach in the problem of gait recognition on unconstrained paths.
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