Efficient likelihood Bayesian constrained local model

Abstract

The constrained local model (CLM) proposes a paradigm that the locations of a set of local landmark detectors are constrained to lie in a subspace, spanned by a shape point distribution model (PDM). Fitting the model to an object involves two steps. A response map, which represents the likelihood of locations for a landmark, is first computed for each landmark using local-texture detectors. Then, an optimal PDM is determined by jointly maximizing all the response maps simultaneously, with a global-shape constraint. This global optimization can be considered a Bayesian inference problem, where the posterior distribution of the shape parameters, as well as the pose parameters, can be inferred using maximum a posteriori (MAP). In this paper, based on the CLM model, we present a novel CLM variant, which employs random-forest regressors to estimate the location of each landmark, as a likelihood term, efficiently. This novel CLM framework is called efficient likelihood Bayesian constrained local model (elBCLM). Furthermore, in each stage of the regressors, the PDM local non-rigid parameters, i.e. the shape parameters, of the previous stage can work as shape clues for training the regressors for the current stage. To further improve the efficiency, we also propose a feature-switching scheme used in the cascaded framework. Experimental results on benchmark datasets show our approach achieves about 3 to 5 times speed-up, when compared with the existing CLM models, and improves by around 10% on fitting accuracy, when compared with the other regression-based models.