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Recognition of human beings using shapes of their full facial surfaces is a difficult problem. Our approach is to approximate a facial surface using a collection of (closed) facial curves, and to compare surfaces by comparing their corresponding curves. The method is further strengthened by the use of texture maps (video images) associated with these faces. Using the commonly used spectral representation of a texture image, i.e. filter images using Gabor filters and compute histograms as image representations, we can compare texture images by comparing their corresponding histograms using the chi-squared distance. A combination of shape and texture metrics provides a method to compare textured, facial surfaces, and we demonstrate its application in face recognition using 240 facial scans of 40 subjects.

HUMAN IDENTIFICATION USING FACIAL CURVES WITH EXTENSIONS TO JOINT SHAPE-TEXTURE ANALYSIS