Face Recognition by SVMs Classification and Manifold Learning of 2D and 3D Radial Geodesic Distances

Abstract

An original face recognition approach based on 2D and 3D Radial Geodesic Distances (RGDs), respectively computed on 2D face images and 3D face models, is proposed in this work. In 3D, the RGD of a generic point of a 3D face surface is computed as the length of the particular geodesic that connects the point with a reference point along a radial direction. In 2D, the RGD of a face image pixel with respect to a reference pixel accounts for the difference of gray level intensities of the two pixels and the Euclidean distance between them. Support Vector Machines (SVMs) are used to perform face recognition using 2D- and 3D-RGDs. Due to the high dimensionality of face representations based on RGDs, embedding into lower-dimensional spaces using manifold learning is applied before SVMs classification. Experimental results are reported for 3D-3D and 2D-3D face recognition using the proposed approach.