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Human Body Part Selection by Group Lasso of Motion for Model-Free Gait Recognition

Abstract : Gait recognition is an emerging biometric technology that identifies people through the analysis of the way they walk. The challenge of model-free based gait recognition is to cope with various intra-class variations such as clothing variations, carrying conditions and angle variations that adversely affect the recognition performance. This paper proposes a method to select the most discriminative human body part based on group Lasso of motion to reduce the intra-class variation so as to improve the recognition performance. The proposed method is evaluated using CASIA Gait Dataset B. Experimental results demonstrate that the proposed technique gives promising results.
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Contributor : Imad Rida Connect in order to contact the contributor
Submitted on : Thursday, January 6, 2022 - 4:59:36 PM
Last modification on : Wednesday, March 2, 2022 - 10:10:12 AM
Long-term archiving on: : Thursday, April 7, 2022 - 7:50:05 PM

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Imad Rida, Xudong Jiang, Gian Marcialis. Human Body Part Selection by Group Lasso of Motion for Model-Free Gait Recognition. IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2016, 23 (1), pp.154-158. ⟨10.1109/LSP.2015.2507200⟩. ⟨hal-03515417⟩

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