Shape localization by statistical learning in the Gabor feature space

Accurate localization of representative points of a face is crucial to many face analysis and synthesis problems. Active shape model (ASM) is a powerful statistical tool for face alignment. However, it suffer from variations of pose, illumination and expressions. In this paper, we propose an improved ASM method, RG-ASM in which local appearance models of key points are modeled using statistical learning. RealBoost is proposed to build the likelihood model that ensures the ground truth position of each key point will more likely have a higher likelihood than its neighbors. Instead of using principle components analysis and one pixel Gabor coefficients. Gabor wavelet features of key point and its neighbors are used as the feature space in the learning procedure to model local structures of a face. Experimental results demonstrate that RG-ASM achieves more accurate results compared with original method used in ASM.