Abstract
Many studies have confirmed gait as a robust biometric feature for identification of individuals. However, direction changes cause difficulties for most of the gait recognition systems, due to appearance changes. This study presents an efficient multi-view gait recognition method that allows curved trajectories on unconstrained paths in indoor environments. The recognition is based on volumetric analysis of the human gait, to exploit most of the 3D information enclosed in it. Appearance-based gait descriptors are extracted from 3D gait volumes and temporal patterns of them are classified using a Support Vector Machine with a sliding temporal window for majority voting. The proposed approach is experimentally validated on the “AVA Multi-View Dataset (AVAMVG)” and on the “Kyushu University 4D Gait Database (KY4D)”. The results show that this new approach is able to identify people walking on curved paths.
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