Geometric features of 3D face and recognition of it by PCA

Abstract

The extraction algorithms for geometric features of 3D face and recognition of the face by PCA (Principal Component Analysis) is proposed. Firstly, by normalizing the original scattered 3D face point cloud, much less amount of the points is acquired, which still contains the main characteristics of the face. Secondly, by calculating and analyzing the curvatures of pre-processed 3D face profiles, which are extracted from the normalized point cloud, the facial feature points are located. And then the 3D geometric features are obtained by the facial feature points. Finally, some merging strategies are performed to recognize the face, where we use the 3D geometric features and implement the scheme of 2D PCA. The experimental results on 3DFACEXMU and ZJU-3DFED databases showed that, the merging strategy which uses the identification results via the geometric features then to screen out the candidates for there cognition by PCA performed on 2D equalized gray image improves recognition accuracy, and the strategy is more robust on expression changes