In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer aided face modeling. First, new face images or new 3D face models can be registered automatically by computing dense one-to-one correspondence to an internal face model. Second, the approach regulates the naturalness of modeled faces avoiding faces with an "unlikely" appearance. Starting from

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. æ 1

Abstract. In this paper we present a method to derive 3D shape and surface texture of a human face from a single image. The method draws on a general flexible 3D face model which is “learned ” from examples of individual 3D-face data (Cyberware-scans). In an analysis-by-synthesis loop, the flexible model is matched to the novel face image. From the coloured 3D model obtained by this procedure, we can generate new images of the face across changes in viewpoint and illumination. Moreover, nonrigid transformations which are represented within the flexible model can be applied, for example changes in facial expression. The key problem for generating a flexible face model is the computation of dense correspondence between all given 3D example faces. A new correspondence algorithm is described which is a generalization of common algorithms for optic flow computation to 3D-face data. 1

In this chapter we present a system for face recognition that combines two recent advances in computer graphics and computer vision: 3D morphable models and component-based recognition. By fitting a morphable model to a triplet of face images we generate a 3D head model for each person in our face database. The 3D models are rendered under varying pose and illumination conditions to build a large set of synthetic images. We then train a component-based face recognition system on these synthetic images. At runtime, the face recognition module is preceded by a hierarchical face detector resulting in a system that can detect and identify faces in video images at about 4 Hz. The system achieved a recognition rate which was significantly higher than that of a comparable global face recognition system trained on the same data. Finally, we address the problem of how to automatically determine the size and shape of facial components for face identification. The need for a robust, accurate, and easily trainable face recognition system becomes more pressing as real world applications in the areas of law enforcement, surveillance, access control, and human machine interfaces continue to develop. However, extrinsic imaging parameters such as pose,