Face Recognition Based on Fitting a 3D Morphable Model-英文文献.pdf

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 25, NO. 9, SEPTEMBER 2003 1063 Face Recognition Based on Fitting a 3D Morphable Model Volker Blanz and Thomas Vetter, Member, IEEE Abstract—This paper presents a method for face recognition across variations in pose, ranging from frontal to profile views, and across a wide range of illuminations, including cast shadows and specular reflections. To account for these variations, the algorithm simulates the process of image formation in 3D space, using computer graphics, and it estimates 3D shape and texture of faces from single images. The estimate is achieved by fitting a statistical, morphable model of 3D faces to images. The model is learned from a set of textured 3D scans of heads. We describe the construction of the morphable model, an algorithm to fit the model to images, and a framework for face identification. In this framework, faces are represented by model parameters for 3D shape and texture. We present results obtained with 4,488 images from the publicly available CMU-PIE database and 1,940 images from the FERET database. Index Terms—Face recognition, shape estimation, deformable model, 3D faces, pose invariance, illumination invariance. 1 INTRODUCTION N face recognition from images, the gray-level or color Two-dimensional face models represent gray values Ivalues provided to the recognition system depend not and their image locations independently [3], [4], [18], [23], only on the identity of the person, but also on parameters [13], [22]. These models, however, do not distinguish such as he