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

Sagar More | Zendy; Pramod Jagan Deore | Zendy

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

Author(s) -

Sagar More,

Pramod Jagan Deore

Publication year - 2017

Publication title -

journal of king saud university - computer and information sciences

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.617

H-Index - 33

eISSN - 2213-1248

pISSN - 1319-1578

DOI - 10.1016/j.jksuci.2017.09.005

Subject(s) - artificial intelligence , pattern recognition (psychology) , wavelet , feature (linguistics) , gait , coherence (philosophical gambling strategy) , computer vision , biometrics , euclidean distance , computer science , feature extraction , linear discriminant analysis , wavelet transform , representation (politics) , mathematics , statistics , physical medicine and rehabilitation , medicine , philosophy , linguistics , politics , political science , law

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.

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