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.