Publications

@article{meher2023surveyarxivb,

title = {A survey and classification of face alignment methods based on face models},

author = {Jagmohan Meher and Hector Allende-Cid and Torbjörn E. M. Nordling},

doi = {10.48550/arXiv.2311.03082},

year  = {2023},

date = {2023-11-07},

journal = {arXiv preprint},

publisher = {Cornell University},

abstract = {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.},

keywords = {Face alignment, face models, heatmaps, morphable models, point distribution models},

pubstate = {published},

tppubtype = {article}

}