Geodesic Distances for 3D-3D and 2D-3D Face Recognition

In this paper, we propose an original framework for representing 2D and 3D face information using geodesic distances. This aims to define a representation enabling the direct comparison between 2D face images of an individual against its 3D face model. This representation is extracted by measuring geodesic distances in 2D and 3D. In 3D, the geodesic distance between two points on a surface is computed as the length of the shortest path connecting the two points. In 2D, the geodesic distance between two pixels is computed based on the differences of gray level intensities along the segment connecting the two pixels. Experimental results are shown to demonstrate the viability of the proposed solution.