Explicit Shape Regression With Characteristic Number for Facial Landmark Localization

Abstract

Robustly localizing facial landmarks plays a very important role in many multimedia and vision applications. Most recently proposed regression-based methods prevailing in the community lack explicit shape constraints for faces and require a large number of facial images to cover great appearance variations. To address these limitations, this paper introduces a novel projective invariant called characteristic number (CN) to explicitly characterize the intrinsic geometries of facial points shared by human faces. It can be verified that the shape priors from CN are inherently invariant to pose changes. By further developing a shape-to-gradient regression framework, we provide a robust and efficient landmark detector for facial images in the wild. The computation of our model can be successfully addressed by learning the descent directions using point-CN pairs without the need for large collections for appearance training. As a nontrivial byproduct, this paper also builds a face dataset, where each face has 15 well-defined viewpoints (poses) to quantitatively analyze the effects of different poses on localization methods. Extensive experiments on challenging benchmarks and our newly built dataset demonstrate the effectiveness of our proposed detector against other state-of-the-art approaches.