Bayesian constrained local models with depth data
This paper proposes an extended Constrained Local Model (CLM) formulation for aligning faces using depth information. The CLMs are popular methods that were initially designed to locate facial features in regular intensity images. Briefly, they combine a set of local detectors, one for each landmark, whose locations are regularized by a linear shape model. Fitting a CLM is usually framed as a two step approach: locally search, using the detectors, producing response maps (likelihood maps) followed by a global optimization strategy that jointly maximize all detection scores while enforcing an appropriate shape. Including depth data could be simply posed as adding additional likelihood sources to the main formulation. The paper discusses several likelihood fusion techniques and propose to jointly learn a multi-dimensional correlation filter as a more reliable solution. Moreover, we propose to learn the local detectors, in the Fourier domain, effectively augmenting the training set with virtual samples. Besides improving the detections reliability, this approach is particular important when applied to depth data, as no additional processing is required (such as fill missing information). The performance evaluation shows that our extended approach further increases the fitting performance (accuracy) effectively proving the benefit of using depth data in facial alignment tasks.