Random forest regressor has recently been proposed as a local landmark estimator in the face alignment problem. It has been shown that random forest regressor can achieve accurate, fast, and robust performance when coupled with a global face-shape regularizer. In this paper, we extend this approach and propose a new Local Forest Classification and Regression (LFCR) framework in order to handle face images with large yaw angles. Specifically, the LFCR has an additional classification step prior to the regression step. Our experiment results show that this additional classification step is useful in rejecting outliers prior to the regression step, thus improving the face alignment results. We also analyze each system component through detailed experiments. In addition to the selection of feature descriptors and several important tuning parameters of the random forest regressor, we examine different initialization and shape regularization processes. We compare our best outcomes to the state-of-the-art system and show that our method outperforms other parametric shape-fitting approaches.

Active Shape Model (ASM) is a powerful statistical tool for image interpretation, especially in face alignment. In the standard ASM, local appearances are described by intensity profiles, and the model parameter estimation is based on the assumption that the profiles follow a Gaussian distribution. It suffers from variations of poses, illumination, expressions and obstacles. In this paper, an improved ASM framework, GentleBoost based SIFT-ASM is proposed. Local appearances of landmarks are originally represented by SIFT (Scale-Invariant Feature Transform) descriptors, which are gradient orientation histograms based representations of image neighborhood. They can provide more robust and accurate guidance for search than grey-level profiles. Moreover, GentleBoost classifiers are applied to model and search the SIFT features instead of the unnecessary assumption of Gaussian distribution. Experimental results show that SIFT-ASM significantly outperforms the original ASM in aligning and localizing facial features.