Locating facial landmarks with binary map cross-correlations

Precise facial landmark localization in still images is a key step for many face analysis applications, such as biometrics or automatic emotion recognition. In this paper, we propose a framework for facial point detection in frontal and near-frontal images. We introduce a new appearance model based on binary map cross-correlations that efficiently uses LBP and LPQ in a localization context. Inclusion of shape-related constraints is performed by a nonparametric voting method using relational properties within triplets of points, designed to correct outliers without losing precision for accurately detected points. We tested our system´s performance on the widely used as benchmark BioID database obtaining state-of-the-art results. We also discuss evaluation metrics used to compare facial landmarking systems and which have been mixed up in recent literature.