A new approach for multi-view gait recognition on unconstrained paths

• We propose a new approach for multi-view gait recognition.
• The method is based on the analysis of 3D reconstructions of gait sequences.
• A new rotation invariant gait descriptor, based on 3D angular analysis is proposed.
• Experimental results demonstrate the effectiveness on unconstrained paths.
• The system can correctly identify about 99% of individuals on two public datasets.

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.