Region-Based Face Alignment with Convolution Neural Network Cascade

Abstract

Most face alignment approaches perform landmark detection over the entire face. However, it has been shown that the difficulty for landmark detection is unbalanced among different facial parts. Thus, in this paper, we propose a novel region-based facial landmark detection algorithm based on a two-level convolutional neural networks (CNNs). In the first level, we partition the whole face into four regions including three facial components (eyebrow-eyes, nose, and mouth) and the face contour. Regions are detected through an improved CNN model which is incorporated with a feature fusion scheme. To simultaneously detect three facial components and face contour landmarks, a novel weighted loss function combining bounding box regression with landmark localization is presented. In the second level, the landmarks are separately detected for three facial components. Experimental results on the public benchmarks demonstrate the superiority of the proposed algorithm over several state-of-the-art face alignment algorithms.