Abstract Human gait is a useful biometric feature for human identification because it can be perceived remotely without physical contact. One critical step for human gait recognition is to accurately extract visual features. In this paper, we apply the center-symmetric local ternary pattern for feature extraction to identify the person from the gait images. The classification is performed by using a support vector machine. Experiments on the CASIA gait database (Dataset B) are given to illustrate the feasibility of the proposed approach.
Human Gait Recognition Using GEI-Based Local Texture Descriptors
Advances in Smart Vehicular Technology, Transportation, Communication and Applications ; 3 ; 292-297
2018-12-01
6 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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