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Communication Dans Un Congrès Année : 2016

Robust model-free gait recognition by statistical dependency feature selection and Globality-Locality Preserving Projections

Résumé

Gait recognition aims to identify people through the analysis of the way they walk. The challenge of modelfree based gait recognition is to cope with various intra-class variations such as clothing variations and carrying conditions that adversely affect the recognition performances. This paper proposes a novel method which combines Statistical Dependency (SD) feature selection with Globality-Locality Preserving Projections (GLPP) to alleviate the impact of intra-class variations so as to improve the recognition performances. The proposed method has been evaluated using CASIA Gait database (Dataset B) under variations of clothing and carrying conditions. The experimental results demonstrate that the proposed method achieves a Correct Classification Rate (CCR) up to 86% when compared to existing state-of-the-art methods.
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Dates et versions

hal-03515467 , version 1 (19-01-2022)

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Imad Rida, Larbi Boubchir, Noor Al-Maadeed, Somaya Al-Maadeed, Ahmed Bouridane. Robust model-free gait recognition by statistical dependency feature selection and Globality-Locality Preserving Projections. 2016 39th International Conference on Telecommunications and Signal Processing (TSP), Jun 2016, Vienna, Austria. pp.652-655, ⟨10.1109/TSP.2016.7760963⟩. ⟨hal-03515467⟩
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