A Region-based Robust 3D Face Recognition

Abstract

We present a region-based robust 3D face recognition approach which is robust to facial expressions, illumination changes and occlusions. Facial surface is often deformed by expressions. Generally, the mouth is the most affected by expressions, whereas the nose is the least affected and the most static region. For this reason, we have concentrated on locating the nose tip and segmenting the nose region. Our method can be grouped into two types: The surface-based approach which uses curvature information of the face and the statistical-based approach which uses subspace analysis. A new algorithm based on the combination of these two types of approaches is presented in this paper. The algorithm extracts the curvature information of the nose region from range image first, by decomposing into maximum and minimum curvature, and then applies PCA (Principal Component Analysis) to reduce the dimension of feature space, respectively. The two features are fused using the sum rule. Our results show that the utilization of the cropped nose region increases the recognition accuracy up to 96.1 percent, where a subset taken from GavabDB database is used to make evaluations.