3D Model Based Face Recognition Using Inverse Compositional Image Alignment

Abstract

3D model based approach for face recognition has been investigated as a robust solution for pose and illumination variation. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time and complexity. In this paper we propose a novel 3D face representation algorithm based on pixel to vertex map (PVM) to reduce number of vertices. We explore shape and texture coefficient vectors of the model by fitting it to an input face using inverse compositional image alignment (ICIA) to evaluate face recognition performance. Experimental results show that proposed face recognition system is efficient in computation time while maintaining reasonable accuracy.