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In this paper, we describe a novel 3-D face recognition scheme for 3-D face recognition that can automatically identify faces from range images, and is insensitive to holes, facial expression, and hair. In our scheme, a number of carefully selected range images constitute a set of example faces, and another range image is chosen as a ldquogeneric face.rdquo The generic face is then warped to match each of the example faces in the least mean square sense. Each such warp is specified by a vector of displacement values. In feature extraction operation, when a target face image comes in, the generic face is warped to match it. The geometric transformation used in the warping is a linear combination of the example face warping vectors. The coefficients in the linear combination are adjusted to minimize the root mean square error. After the matching process is complete, the coefficients of the composite warp are used as features and passed to a Mahalanobis-distance-based classifier for face recognition. Our technique is tested on a data set containing more than 600 range images. Experimental results in the access-control scenario show the effectiveness of the extracted features.