Face recognition from 2D and 3D images using structural Hausdorff distance

This paper presents a view invariant face recognition system based on both 3D range data as well as 2D gray-level facial images. An irregular 2D mesh labeled by twelve landmarks and 3D regions labeled by four landmarks are defined for each model for feature extraction. Nodes of the 2D mesh are described by Gabor filter responses and 3D points are represented by point signature. To classify test faces under varying poses from one stored view, a robust structural Hausdorff distance is proposed for non-point-to-point matching under the structural constraints. The best matched model is determined based on the linear integration of matching results in 2D and 3D domains. Experimental results based on our database involving 80 persons with different facial expression and different viewpoint have demonstrated the efficiency of our algorithm.