Direction changes cause di culties 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 e ect, 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 classi cation 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 e ectiveness of this approach in the problem of
gait recognition on unconstrained path
