Face image analysis using a multiple features fitting strategy

The main contribution of this thesis is a novel algorithm for fitting a Three-Dimensional Morphable Model of faces to a 2D input image. This fitting algorithm enables the estimation of the 3D shape, the texture, the 3D pose and the light direction from a single input image. Generally, the algorithms tackling the problem of 3D shape estimation from image data use only the pixels intensity as input to drive the estimation process. This was previously achieved using either a simple model, such as the Lambertian reflectance model, leading to a linear fitting algorithm. Alternatively, this problem was addressed using a more precise model and minimizing a non-convex cost function with many local minima. One way to reduce the local minima problem is to use a stochastic optimization algorithm. However, the convergence properties (such as the radius of convergence) of such algorithms, are limited. Here, as well as the pixel intensity, we use various image features such as the edges or the location of the specular highlights. The 3D shape, texture and imaging parameters are then estimated by maximizing the posterior of the parameters given these image features. The overall cost function obtained is smoother and, hence, a stochastic optimization algorithm is not needed to avoid the local minima problem. This leads to the Multi-Features Fitting algorithm that has a wider radius of convergence and a higher level of precision. The new Multi-Feature Fitting algorithm is applied for such tasks as face identification, facial expression transfer from one image to another image (of different individuals) and face tracking across 3D pose and expression variations. The second contribution of this thesis is a careful comparison of well known fitting algorithms used in the context of face modelling and recognition. It is shown that these algorithms achieve high run time efficiency at the cost of accuracy and generality (few face images may be analysed). The third and last contribution is the Matlab Morphable Model toolbox, a set of software tools developed in the Matlab programing environment. It allows (i) the generation of 3D faces from model parameters, (ii) the rendering of 3D faces, (iii) the fitting of an input image using the Multi-Features Fitting algorithm and (iv) identification from model parameters. The toolbox has a modular design that allows anyone to builds on it and, for instance, to improve the fitting algorithm by incorporating new features in the cost function.