Surface curvatures such as Gaussian, mean and principal curvatures are intrinsic surface properties and have played important roles in curved surface analysis. In this paper, we present a correlation-based face recognition approach based on the analysis of maximum and minimum principal curvatures and their directions. We treat face recognition problem as a 3D shape recognition problem of free-form curved surfaces. Our approach is based on a 3D vector sets correlation method which does not require either face feature extraction or surface segmentation. Each face in both input images and the model database, is represented as an Extended Gaussian Image (EGI), constructed by mapping principal curvatures and their directions at each surface points, onto two unit spheres, each of which represents ridge and valley lines respectively. Individual face is then recognized by evaluating the similarities among others by using Fisher's spherical correlation on EGI's effaces. The method is tested for its simplicity and robustness and successively implemented for each of face range images from NRCC (National Research Council Canada) 3D image data files. Results show that shape information from surface curvatures provides vital cues in distinguishing and identifying such fine surface structure as human faces.
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