This thesis proposes a robust Automatic Face Verification (AFV) system using Local Binary Patterns (LBP). AFV is mainly composed of two modules: Face Detection (FD) and Face Verification (FV). The purpose of FD is to determine whether there are any face in an image, while FV involves confirming or denying the identity claimed by a person. The contributions of this thesis are the following: 1) a real-time multiview FD system which is robust to illumination and partial occlusion, 2) a FV system based on the adaptation of LBP features, 3) an extensive study of the performance evaluation of FD algorithms and in particular the effect of FD errors on FV performance. The first part of the thesis addresses the problem of frontal FD. We introduce the system of Viola and Jones which is the first real-time frontal face detector. One of its limitations is the sensitivity to local lighting variations and partial occlusion of the face. In order to cope with these limitations, we propose to use LBP features. Special emphasis is given to the scanning process and to the merging of overlapped detections, because both have a significant impact on the performance. We then extend our frontal FD module to multiview FD. In the second part, we present a novel generative approach for FV, based on an LBP description of the face. The main advantages compared to previous approaches are a very fast and simple training procedure and robustness to bad lighting conditions. In the third part, we address the problem of estimating the quality of FD. We first show the influence of FD errors on the FV task and then empirically demonstrate the limitations of current detection measures when applied to this task. In order to properly evaluate the performance of a face detection module, we propose to embed the FV into the performance measuring process. We show empirically that the proposed methodology better matches the final FV performance
