3D FACE RECOGNITION UNDER EXPRESSIONS, OCCLUSIONS AND POSE VARIATION

Electronics & Telecommunication Engineering

We propose a novel geometric framework for analysing 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 analysing 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, and illustrates the use of radial facial curves on 3D meshes to mode facial deformation caused by expression, occlusion and variation in poses and to recognize faces despite large expression, in presence of occlusion and pose variations. Here we represent facial surface by indexed collection of radial geodesic curves on 3D face meshes emanating from nose tip to the boundary of mesh and compare the facial shapes by comparing shapes of their corresponding curves. We use elastic shape analysis for comparing shapes of facial curves because elastic matching seems natural for facial deformation 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, and so on. Our results match or improve upon the state-of-the-art methods on two prominent databases: GavabDB and Bosporus, each posing a different type of challenges.

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