Sign correlation subspace for face alignment

Face alignment is an essential task for facial performance capture and expression analysis. Current methods such as random subspace supervised descent method, stage-wise relational dictionary and coarse-to-fine shape searching can ease multi-pose face alignment problem, but no method can deal with the multiple local minima problem directly. In this paper, we propose a sign correlation subspace method for domain partition in only one reduced low-dimensional subspace. Unlike previous methods, we analyze the sign correlation between features and shapes and project both of them into a mutual sign correlation subspace. Each pair of projected shape and feature keeps their signs consistent in each dimension of the subspace, so that each hyper octant holds the condition that one general descent exists. Then a set of general descents are learned from the samples in different hyperoctants. Requiring only the feature projection for domain partition, our proposed method is effective for face alignment. We have validated our approach with the public face datasets which include a range of poses. The validation results show that our method can reveal their latent relationships to poses. The comparison with state-of-the-art methods demonstrates that our method outperforms them, especially in uncontrolled conditions with various poses, while enjoying the comparable speed.