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Learning weighted sparse representation of encoded facial normal information for expression-robust 3D face recognition

Abstract : This paper proposes a novel approach for 3D face recognition by learning weighted sparse representation of encoded facial normal information. To comprehensively describe 3D facial surface, three components, in X, Y, and Z-plane respectively, of normal vector are encoded locally to their corresponding normal pattern histograms. They are finally fed to a sparse representation classifier enhanced by learning based spatial weights. Experimental results achieved on the FRGC v2.0 database prove that the proposed encoded normal information is much more discriminative than original normal information. Moreover, the patch based weights learned using the FRGC v1.0 and Bosphorus datasets also demonstrate the importance of each facial physical component for 3D face recognition.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01354565
Contributor : Équipe Gestionnaire Des Publications Si Liris <>
Submitted on : Thursday, August 18, 2016 - 7:33:04 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:45 PM

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Huibin Li, Di Huang, Jean-Marie Morvan, Liming Chen. Learning weighted sparse representation of encoded facial normal information for expression-robust 3D face recognition. 2011 International Joint Conference on Biometrics (IJCB), , Oct 2011, Washington, DC, United States. pp.1-7, ⟨10.1109/IJCB.2011.6117555⟩. ⟨hal-01354565⟩

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