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The ability to distinguish different people by using 3D facial information is an active research problem being undertaken by the face recognition community. In this paper, we propose to use a generic model to label the 3D facial features. This approach relies on our realistic face modeling technique, by which the individual face model is created using a generic model and two views of a face. In the individualized model, we label the face features by their maximum and minimum curvatures. Among the labeled features, the "good features" are selected by using a Genetic Algorithm based approach. The feature space is then formed by using these new 3D shape descriptors, and each individual face is classified according to its feature space correlation. We applied 72 individual models for the test. The experimental results show that the shape information obtained from the 3D individualized model can be used to classify and identify individual facial surfaces. The rank-four correct recognition rate is about 92%. The 3D individualized model provides consistent and sufficient details to represent individual faces while using a much more simpli?ed representation than the range data models. This work provides the possibility to reduce the complexity of 3D data processing, and is feasible for real applications such as in a non-cooperative imaging circumstance or in the situation when range data using 3D scanners are not possible to acquire.