This paper proposes a discriminative framework for efficiently aligning images. Although conventional Active Appearance Models (AAM)-based approaches have achieved some success, they suffer from the generalization problem, i.e., how to align any image with a generic model. We treat the iterative image alignment problem as a process of maximizing the score of a trained two-class classifier that is able to distinguish correct alignment (positive class) from incorrect alignment (negative class). During the modeling stage, given a set of images with ground truth landmarks, we train a conventional Point Distribution Model (PDM) and a boosting-based classifier, which we call Boosted Appearance Model (BAM). When tested on an image with the initial landmark locations, the proposed algorithm iteratively updates the shape parameters of the PDM via the gradient ascent method such that the classification score of the warped image is maximized. The proposed framework is applied to the face alignment problem. Using extensive experimentation, we show that, compared to the AAM-based approach, this framework greatly improves the robustness, accuracy and efficiency of face alignment by a large margin, especially for unseen data. 1.

Pose, illumination, expression and the generalization of such effects to unseen face data samples are the fundamental problems faced in face recognition. The significant contribution of this thesis is the ability to match any two face images with a large pose angle variation. This approach utilizes a proposed 3D prior face model in order to cover a wide range of poses. To achieve this, a rapid 3D modeling scheme is proposed, called 3D Generic Elastic Model (GEM), which allows the synthesis of novel 2D images faster and more realistically than traditional 3D Morphable Model (3DMM) approaches used to date. In contrast, our work only requires the observed facial landmarks in a face image (see Appendix A for proposed work in robust facial landmarking and alignment using combined Active Shape and Active Appearance Models), coupled with the proposed 3D GEM depth-map generated from the USF Human-ID database. Although we only use a single GEM, we show that we can model a diverse set of 3D dense face shapes which provide visually accurate novel 2D pose synthesis of faces. Indeed, we show that our 3D models can be successfully applied not only to 2D pose synthesis but also to novel illumination synthesis. The proposed modeling approach is fully automatic, robust,

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