Gabor filter bank-based GEI features for human Gait recognition

Ait Lishani, Larbi Boubchir, Emad Khalifa, Ahmed Bouridane

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

5 Citations (Scopus)

Abstract

This paper proposes a supervised feature extraction approach which is capable to select distinctive features for the recognition of human gait under clothing and carrying conditions thus improving the recognition performances. The principle of the suggested approach is based on the use of feature texture descriptors extracted from Gait Energy Image (GEI). The proposed features are computed using the bank of Gabor filters and then selected using Spectral Regression Kernel Discriminant Analysis (SRKDA) reduction algorithm. The proposed method is evaluated on CASIA Gait database (dataset B) under variations of clothing and carrying conditions for different viewing angles; and the experimental results using one-against-all SVM classifier have given attractive results of up to 91% in terms of Correct Classification Rate (CCR) when compared to existing and similar state-of-the-art methods.
Original languageEnglish
Title of host publicationProceedings of the 39th International Conference on Telecommunications and Signal Processing (TSP)
PublisherIEEE
Pages648-651
ISBN (Print)978-1-5090-1288-6
DOIs
Publication statusPublished - 1 Dec 2016
Event39th International Conference on Telecommunications and Signal Processing (TSP), 2016 - Vienna
Duration: 1 Dec 2016 → …

Conference

Conference39th International Conference on Telecommunications and Signal Processing (TSP), 2016
Period1/12/16 → …

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