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

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Abstract

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.

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Authors and Affiliations

  1. Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne, NE2 1XE, UK

    Ait O. Lishani, Emad Khalifa & Ahmed Bouridane

  2. LIASD Laboratory, Department of Computer Science, University of Paris 8, 2 rue de la Liberté, 93526, Saint-Denis cedex, France

    Larbi Boubchir

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  1. Ait O. Lishani
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  2. Larbi Boubchir
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  3. Emad Khalifa
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  4. Ahmed Bouridane
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Correspondence to Larbi Boubchir.

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Lishani, A.O., Boubchir, L., Khalifa, E. et al. Human gait recognition using GEI-based local multi-scale feature descriptors. Multimed Tools Appl 78, 5715–5730 (2019). https://doi.org/10.1007/s11042-018-5752-8

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  • DOI: https://doi.org/10.1007/s11042-018-5752-8

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