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

Ait O. Lishani, Larbi Boubchir*, Emad Khalifa, Ahmed Bouridane

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)5715-5730
Number of pages16
JournalMultimedia Tools and Applications
Issue number5
Early online date14 Feb 2018
Publication statusPublished - 1 Mar 2019


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