3D face modeling from 2D face images is of significant importance for face analysis, animation and recognition. Previous research on this topic mainly focused on 3D face modeling from a single 2D face image; however, a single face image can only provide a limited description of a 3D face. In many applications, for example, law enforcement, multi-view face images are usually captured for a subject during enrollment, which makes it desirable to build a 3D face texture model, given a pair of frontal and profile face images. We first determine the correspondence between uncalibrated frontal and profile face images through facial landmark alignment. An initial 3D face shape is then reconstructed from the frontal face image, followed by shape refinement utilizing the depth information provided by the profile image. Finally, face texture is extracted by mapping the frontal face image on the recovered 3D face shape. The proposed method is utilized for 2D face recognition in two scenarios: (i) normalization of probe image, and (ii) enhancing the representation capability of gallery set. Experimental results comparing the proposed method with a state-of-the-art commercial face matcher and densely sampled LBP on a subset of the FERET database show the effectiveness of the proposed 3D face texture model. 1.

This thesis presents research aimed at improving the quality of facial shape information that can be recovered from single intensity images using shape-from-shading, with the aim of exploiting this information for the purposes of face recognition and view synthesis. The common theme throughout this thesis is the use of statistical methods to offer enhanced accuracy and robustness over existing techniques for facial shape-from-shading. The work presented goes some way to reinstating shape-from-shading as a viable means to recover facial shape from single, real world images. In Chapter 2 we thoroughly survey the existing literature in the areas of face recognition, shape recovery and skin reflectance modelling. We draw from this review a number of important observations. The first is that existing solutions to the general shape-fromshading problem prove incapable of recovering accurate facial shape from real world images. The second is that statistical models have been shown to be highly effective in modelling facial appearance and shape variation and have been applied successfully to the problem of face recognition. Finally, we highlight the complex nature of light interaction with skin and note that previous attempts to apply shape-from-shading to real world face

We focus on the problem of developing a coupled statistical model that can be used to recover facial shape from brightness images of faces. We study three alternative representations for facial shape. These are the surface height function, the surface gradient and a Fourier basis representation. We jointly capture variations in intensity and the surface shape representations using a coupled statistical model. The model is constructed by performing principal components analysis (PCA) on sets of parameters describing the contents of the intensity images and the facial shape representations. By fitting the coupled model to intensity data, facial shape is implicitly recovered from the shape parameters. Experiments show that the coupled model is able to generate accurate shape from out-of-training-sample intensity images. Shape-from-shading, statistical models, face analysis. Index Terms I.