Face Alignment Based on Local Shapes Constraint Network

Abstract:

Face alignment method has greatly improved with using cascade regression, however due to the complexity of designing cascade regressors, the limitation of hand-crafted features make it difficult to find a better solution for face alignment task, especially with the big gesture, exaggerated expressions. Therefore, this paper proposes a novel method based on local shape constraint to solve this problem. Firstly it initializes the whole face shape by using deep convolutional neural networks (DCNN), secondly divides face into different regions according to the local regional homogeneity, defining each region constraints on local shapes, Finally takes the whole shape estimation as global constraints, combines each local shape constraints for facial feature point regression. The experiments show that our method based on local shapes constraint results in a strong improvement over the current state-of-the-art.