The accuracy of a three-dimensional (3-D) face recognition system depends on a correct registration that aligns the facial surfaces and makes a comparison possible. The best results obtained so far use a costly one-to-all registration approach, which requires the registration of each facial surface to all faces in the gallery. We explore the approach of registering the new facial surface to an average face model (AFM), which automatically establishes correspondence to the preregistered gallery faces. We propose a new algorithm for constructing an AFM and show that it works better than a recent approach. We inspect thin-plate spline and iterative closest-point-based registration schemes under manual or automatic landmark detection prior to registration. Extending the single-AFM approach, we consider employing categoryspecific alternative AFMs for registration and evaluate the effect on subsequent classification. We perform simulations with multiple AFMs that correspond to different clusters in the face shape space and compare these with gender- and morphology-based groupings. We show that the automatic clustering approach separates the faces into gender and morphology groups, consistent with the other race effect reported in the psychology literature. Last, we describe and analyze a regular resampling method, that significantly increases the accuracy of registration.