Abstract. Face alignment, which is the task of finding the locations of a set of facial landmark points in an image of a face, is useful in widespread application areas. Face alignment is particularly challenging when there are large variations in pose (in-plane and out-of-plane rotations) and facial expression. To address this issue, we propose a cascade in which each stage consists of a mixture of regression experts. Each expert learns a customized regression model that is specialized to a different subset of the joint space of pose and expressions. The system is invariant to a predefined class of transformations (e.g., affine), because the input is transformed to match each expert's prototype shape before the regression is applied. We also present a method to include deformation constraints within the discriminative alignment framework, which makes our algorithm more robust. Our algorithm significantly outperforms previous methods on publicly available face alignment datasets.
A cascade of a mixture of regressors was suggested by Tuzel et al. REF , where each regressor learns a regression model adapted to a particular subspace of pose and expressions, such as a smiling face turned to the left.
Robust Face Alignment Using a Mixture of Invariant Experts
Proceedings of 14th European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands, October 11-14, 2016, pp 825-841
Computer Science