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Abstract
This study aimed to develop a vision-based gait recognition system for person identification. Gait is the soft biometric trait recognizable from low-resolution surveillance videos, where the face and other hard biometrics are not even extractable. The gait is a cycle pattern of human body locomotion that consists of two sequential phases: swing and stance. The gait features of the complete gait cycle, referred to as gait signature, can be used for person identification. The proposed work utilizes gait dynamics for gait feature extraction. For this purpose, the spatio temporal power spectral gait features are utilized for gait dynamics captured through sub-pixel motion estimation, and they are less affected by the subject’s appearance. The spatio temporal power spectral gait features are utilized for a quadratic support vector machine classifier for gait recognition aiming for person identification. Spatio temporal power spectral preserves the spatiotemporal gait features and is adaptable for a quadratic support vector machine classifier-based gait recognition across different views and appearances. We have evaluated the gait features and support vector machine classifier-based gait recognition on a locally collected gait dataset that captures the effect of view variance in high scene depth videos. The proposed gait recognition technique achieves significant accuracy across all appearances and views.
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Lima BN, Balducci P, Passos RP, Novelli C, Fileni CHP, Vieira F, Camargo LBD, Vilela Junior GDB. Artificial intelligence based on fuzzy logic for the analysis of human movement in healthy people: a systematic review. Artif Intell Rev 2020. [DOI: 10.1007/s10462-020-09885-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Gibelli D, Obertová Z, Ritz-Timme S, Gabriel P, Arent T, Ratnayake M, De Angelis D, Cattaneo C. The identification of living persons on images: A literature review. Leg Med (Tokyo) 2016; 19:52-60. [DOI: 10.1016/j.legalmed.2016.02.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2015] [Revised: 01/08/2016] [Accepted: 02/01/2016] [Indexed: 10/22/2022]
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