A survey and classification of face alignment methods based on face models
This is an incremental survey paper that benefits beginners, practitioners, and researchers in face alignment by organizing existing knowledge.
The paper addresses the lack of a review analyzing various face models used in face alignment, providing a comprehensive analysis of different face models, including their interpretation, training, and fitting examples, and found that 3D-based models are preferred for extreme poses while deep learning methods often use heatmaps.
A face model is a mathematical representation of the distinct features of a human face. Traditionally, face models were built using a set of fiducial points or landmarks, each point ideally located on a facial feature, i.e., corner of the eye, tip of the nose, etc. Face alignment is the process of fitting the landmarks in a face model to the respective ground truth positions in an input image containing a face. Despite significant research on face alignment in the past decades, no review analyses various face models used in the literature. Catering to three types of readers - beginners, practitioners and researchers in face alignment, we provide a comprehensive analysis of different face models used for face alignment. We include the interpretation and training of the face models along with the examples of fitting the face model to a new face image. We found that 3D-based face models are preferred in cases of extreme face pose, whereas deep learning-based methods often use heatmaps. Moreover, we discuss the possible future directions of face models in the field of face alignment.