Learning global cost function for face alignment

Face alignment is a crucial step in many facial processing applications. It has received extensive attention in the last two decades. The general approach consist in estimating the parameters of a deformable shape model, which minimize a cost function. Most of the existing methods are based on empirical cost functions. In this paper we propose to learn an ideal global cost function (i.e. the quality of the alignment) as convex as possible in order to lead to a simple and robust optimization step. This learning process relies on the Boosted Input Selection Algorithm for Regression (BISAR). It selects the best set of Haar-like features as input of a Neural Network to predict the value of the cost function. Performance of this method is evaluated on unseen data from the training database. The generalization performance is assessed on unseen data from unrelated datasets. This approach is also favorably compared with a state-of-the-art method.