Gait-based human recognition using partial wavelet coherence and phase features

In this paper, a multi-view human gait recognition method which utilizes Partial Wavelet Coherence (PWC) as a novel feature is proposed. The Euclidean distance representation of PWC of the 1D signals generated due to movements of hands, legs, shoulders from multi-view gait sequences preserves the spatio-temporal information of walking individual. This method directly extracts the dynamic information without using any model. We got 73.26% average recognition accuracy when considered only PWC feature. Further, we investigate Phase Feature (PF) which also preserves discriminant information of dynamic phase angle between body parts. When PF considered additionally with PWC feature the system performance improved significantly and 82.52% average recognition accuracy reported. (C) 2017 The Authors. Production and hosting by Elsevier B.V.