Likelihood-enhanced Bayesian constrained local models

This paper addresses to the problem of aligning images in unseen faces. The Constrained Local Models (CLM) are popular methods that combine a set of local landmark detectors whose locations are constrained to lie in a subspace spanned by a linear shape model. The CLM fitting is usually based on a two step approach: locally search, using the detectors, producing response maps (likelihood) followed by a global optimization strategy that jointly maximize all detections at once. In this paper, we mainly focus on the first stage: improving the detectors reliability. Usually the local landmarks detectors are far from perfect. Most often are designed to be fast, having a small support region and are learnt from limited data. As consequence, they will suffer from detection ambiguities. Here we propose to improve the detectors performance by considering multiple detection per landmark. In particular, we propose a joint learning of the detectors by clustering of their training data. Afterwards, the multiple likelihoods are combined using a nonlinear fusion approach. The performance evaluation shows that our (extended) approach further increases the fitting performance of the CLM formulation, when compared with recent state-of-the-art methods.