Affine-Transformation Parameters Regression for Face Alignment

Abstract

Face alignment is an important process in facial analysis. Cascaded linear regression approaches have shown the capability to achieve the state-of-the-art accuracy on numerous face alignment datasets. However, most of these approaches only learn to map coordinate offsets of the key points from image features. This regression strategy can be easily trapped in local optima. We propose a novel regression strategy by introducing affine transformation. First, the best affine-transformation parameters between the initial mean shape and the ground truth are estimated by Procrustes analysis. Subsequently, we base the mapping from image features on the best affine-transformation parameters. Experimental results indicate that this strategy can reduce the offsets between two shapes significantly. Combined with coordinate-offset regression strategy, the hybrid approach produces a remarkably performance in term of accuracy, training time, prediction rate, and the model size. Moreover, the affine-transformation parameter regression strategy can be considered as a shape-initialization method that can be combined with other initial shape-based face alignment algorithms to improve the face alignment accuracy.