Human gait recognition using GEI-based local multi-scale feature descriptors

Lishani, Ait O., Boubchir, Larbi, Khalifa, Emad and Bouridane, Ahmed (2019) Human gait recognition using GEI-based local multi-scale feature descriptors. Multimedia Tools and Applications, 78 (5). pp. 5715-5730. ISSN 1380-7501

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Human gait recognition is a biometric technique for persons identification based on their walking manner. This paper proposes a novel gait recognition approach capable of selecting information characteristics for human identification under different conditions including normal walking, carrying a bag and wearing a clothing for different angles of view; thereby enhancing the recognition accomplishment. The proposed approach relies on two feature extraction methods based on multi-scale feature descriptors including Multi-scale Local Binary Pattern (MLBP) and Gabor filter bank, through Spectra Regression Kernel Discriminant Analysis (SRKDA) reduction algorithm. The proposed features are extracted locally from two Region of Interest (ROIs) representing the dynamic areas in the Gait Energy Image (GEI). The experiments conducted on CASIA and USF Gait databases have shown that the suggested methods achieve better recognition performances up to 92% in terms of identification rate at rank-1 than the existing similar and recent state-of-the-art methods.

Item Type: Article
Uncontrolled Keywords: Biometrics, Gabor filter bank, Gait energy image, Gait recognition, Multi-scale local binary pattern, Spectra regression kernel discriminant analysis
Subjects: G400 Computer Science
G600 Software Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 25 Apr 2019 08:15
Last Modified: 10 Oct 2019 19:34

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