3D FACE RECOGNITION UNDER EXPRESSIONS, OCCLUSIONS AND POSE VARIATION
Keywords:
3D Face recognition, data restoration, Geodesic curve, neural network, shape analysis, pca method, eigenfaces, NN methodAbstract
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
