A 3D Facial Feature Point Localization Method Based on Statistical Shape Model

Registration is a necessary step for automatic 3D face recognition systems, and feature point localization is usually used to find the correspondence in registration. Traditional localization methods are sensitive to pose changes, and can only deal with frontal or limited pose variations. In this paper we propose a new 3D facial feature point localization method that is insensitive to pose variation. Feature regions are first segmented out based on shape index features, and then selected by a statistical shape model. Point nearest to the region center is chosen as a feature point. Experimental results show that the localization accuracy is comparable to manually labeled feature points.