Face Recognition and Growth Prediction using a 3D Morphable Face Model

We present two different techniques and applications that are based on the 3D Morphable Face Model. In the first part of this thesis, we develop a new top-down approach to 3D data analysis by fitting a 3D Morphable Face Model to 3D scans of faces. The algorithm is specifically designed for scans which were recorded in a perspective projection. In an analysis-by-synthesis approach, shape, texture, pose and illumination are optimized simultaneously. Starting from raw 3D scans, the algorithm determines a PCA-based representation which fits the scan best. Also, fragmentary surfaces are completed and correspondence to a reference face of the morphable model is established. Simultaneously, illumination conditions are estimated in an explicit simulation that involves specular and diffuse components. The effects of lighting and shading are removed to obtain an illumination corrected texture which stores the diffuse reflectance in each point of the facial surface. We use the algorithm as a core component in 3D face recognition on a subset of the FRGC database of scans. In part two of this thesis we explore the growth of 3D faces, represented in a 3D Morphable Face Model. Assuming that 3D faces follow curved trajectories in face space as they age, we present a novel algorithm that computes individual aging trajectories for given faces. From a database of 3D scans of teenagers and adults, we learn an non-linear function, that assigns an age to each face vector using support vector regression. Computing the gradient of this function leads us to trajectories that describe the direction of growth in face space. Starting from photographs of faces we apply the aging prediction to images of faces by reconstructing a 3D model from the input image and applying the aging transformation on both shape and texture. The resulting face model is rendered back