3D+2D Face Localization Using Boosting in Multi-Modal Feature Space
Feng Xue, Xiaoqing Ding · 2006
Facial feature extraction is important in many face-related applications, such as face alignment for recognition. Recently, boosting-based methods have led to the state-of-the-art face detection and localization systems. In this paper, we propose a multi-modal boosting algorithm to integrate 3D (range) and 2D (intensity) information provided from a facial scan to detect the face and feature point (nose tip, eyes center). Given a face scan, Gauss and mean curvature are calculated. Face, nose and eyes detectors are trained in color images and curvature maps features space using AdaBoost. As a result, a fully automatic multi-modal face location system is developed. The performance evaluation is conducted for the proposed feature extraction algorithm on a publicly available data-base, containing 4007 facial scans of 466 subjects