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We present an accurate and robust framework for detecting and segmenting faces, localizing landmarks, and achieving fine registration of face meshes based on the fitting of a facial model. This model is based on a 3-D Point Distribution Model (PDM) that is fitted without relying on texture, pose, or orientation information. Fitting is initialized using candidate locations on the mesh, which are extracted from low-level curvature-based feature maps. Face detection is performed by classifying the transformations between model points and candidate vertices based on the upper-bound of the deviation of the parameters from the mean model. Landmark localization is performed on the segmented face by finding the transformation that minimizes the deviation of the model from the mean shape. Face registration is obtained using prior anthropometric knowledge and the localized landmarks. The performance of face detection is evaluated on a database of faces and non-face objects where we achieve an accuracy of 99.6%. We also demonstrate face detection and segmentation on objects with different scale and pose. The robustness of landmark localization is evaluated with noisy data and by varying the number of shapes and model points used in the model learning phase. Finally, face registration is compared with the traditional Iterative Closest Point (ICP) method and evaluated through a face retrieval and recognition framework on the GavabDB dataset, where we achieve a recognition rate of 87.4% and a retrieval rate of 83.9%.