Facial Landmark Localization by Part-Aware Deep Convolutional Network

Keke He, Xiangyang Xue

Abstract: Facial landmark localization is a very challenging research task. The localization accuracy of landmarks on separate facial parts differ greatly due to texture and shape, however most existing methods fail to consider the part location of landmarks. To solve this problem, we propose a novel end-to-end regression framework using deep convolutional neural network (CNN). Our deep architecture first encodes the image into feature maps shared by all the landmarks. Then, these features are sent into two independent sub-network modules to regress contour landmarks and inner landmarks, respectively. Extensive evaluations conducted on 300-W benchmark dataset demonstrate the proposed deep framework achieves state-of-the-art results.