Pose-Invariant 2D Face Recognition by Matching Using Graphical Models

The thesis presents a 2D face recognition system using Markov random field matching method-ology for establishing dense correspondences between a pair of images in the presence of pose changes and self-occlusion. The proposed method, which exploits both shape and texture dif-ferences between images, achieves very competitive performance compared to the current ap-proaches. The algorithm bypasses the need for geometric pre-processing of face images. By virtue of the matching methodology embedded in the algorithm, the proposed approach can cope with moderate translation, in and out of plane rotation, scaling and perspective effects. Also by employing a graphical model based approach, the proposed system circumvents the need for non-frontal images being available for training a pose-invariant face recognition sys-tem. In contrast to the state-of-the-art approaches based on 3D models, the approach operates on 2D images and bypasses the need for 3D face training data and avoids the vagaries of 3D face model to 2D face image fitting. From the point of view of object recognition based on graphical models, the matching energy in graph based approaches is shown to exhibit certain drawbacks and should not be used as a similarity criterion for the hypothesis selection directly. The main shortcomings of the energ