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3D Face Recognition Under Expressions,Occlusions and Pose Variations

Abstract : We propose a novel geometric framework for analyzing 3D faces, with the specific goals of comparing, matching, and averaging their shapes. Here we represent facial surfaces by radial curves emanating from the nose tips and use elastic shape analysis of these curves to develop a Riemannian framework for analyzing shapes of full facial surfaces. This representation, along with the elastic Riemannian metric, seems natural for measuring facial deformations and is robust to challenges such as large facial expressions (especially those with open mouths), large pose variations, missing parts, and partial occlusions due to glasses, hair, etc. This framework is shown to be promising from both - empirical and theoretical - perspectives. In terms of the empirical evaluation, our results match or improve the state-of-the-art methods on three prominent databases: FRGCv2, GavabDB, and Bosphorus, each posing a different type of challenge. From a theoretical perspective, this framework allows for formal statistical inferences, such as the estimation of missing facial parts using PCA on tangent spaces and computing average shapes.
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Contributor : Hassen Drira <>
Submitted on : Thursday, January 31, 2013 - 4:45:44 PM
Last modification on : Wednesday, June 24, 2020 - 4:18:53 PM
Document(s) archivé(s) le : Wednesday, May 1, 2013 - 3:56:19 AM


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Hassen Drira, Ben Amor Boulbaba, Srivastava Anuj, Mohamed Daoudi, Rim Slama. 3D Face Recognition Under Expressions,Occlusions and Pose Variations. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2013, pp.2270 - 2283. ⟨halshs-00783066⟩



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