Face recognition by SVMS classification of 2D and 3D Radial Geodesics

An original approach to represent 2D and 3D faces using radial geodesic distances (RGDs) is proposed in this work. In 3D, the RGD of a generic point of the face surface is computed as the length of the geodesic connecting the point with a reference point along a radial direction. In 2D, the RGD of a 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 is applied before SVMs classification. Experimental results are reported for 3D-3D and 2D-3D face recognition using the proposed approach.