Abstract

In this paper, we present a robust face alignment system that is capable of dealing with exaggerating expressions, large occlu- sions, and a wide variety of image noises. The robustness comes from our shape regularization model, which incorporates constrained nonlin- ear shape prior, geometric transformation, and likelihood of multiple candidate landmarks in a three-layered generative model. The inference algorithm iteratively examines the best candidate positions and updates face shape and pose. This model can effectively recover sufficient shape details from very noisy observations. We demonstrate the performance of this approach on two public domain databases and a large collection of real-world face photographs.