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

Huibin Li, Huang Di, Jean-Marie Morvan, Liming Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

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. © 2011 IEEE.
Original languageEnglish (US)
Title of host publication2011 International Joint Conference on Biometrics (IJCB)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)9781457713583
DOIs
StatePublished - Oct 2011

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