A Genetic Algorithm Based Approach for 3D Face Recognition

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 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 face features by their principal curvatures. Among the labeled features, “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 105 individual models for the experiment. 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-4 recognition rate is 92%. The 3D individualized model provides consistent and sufficient details to represent individual faces while using a much more simplified representation than the range data models. To verify the accuracy and robustness of the selected feature spaces, a similar procedure is applied on the range data obtained from the 3D scanner. We used a subset of the optimal feature space derived from the Genetic Algorithm, and achieved an 87% rank-4 recognition rate. It shows that our approach provides a possible way to reduce the complexity of 3D data processing and is feasible to applications using different sources of 3D data.