Face alignment is a key component of numerous face analysis tasks. In recent years, most existing methods have focused on designing high-performance face alignment systems and paid less attention to efficiency.
However more face alignment systems are now applied on low-cost devices, such as mobile phones. In this article, we design a common efficient framework that can team with any face alignment regression network and improve the overall performance with nearly no extra computational cost. First, we discover that the maximum regression error exists in the face contour, where landmarks do not have distinct semantic positions, and thus are randomly labeled along the face contours in training data. To address this problem, we propose a novel contour fitting loss that dynamically adjusts the regression target during training so the
network can learn more accurate semantic meanings of the contour landmarks and achieve better localization performance. Second, we decouple the complex sample variations in face alignment task and propose a Fast Normalization Module (FNM) to efficiently normalize considerable variations that can be described by geometric transformation. Finally, a new lightweight network architecture named Lightweight Alignment Module (LAM) is also proposed to achieve fast and precise face alignment on mobile devices. Our method achieves competitive performance with state-of-the-arts on 300W and AFLW2000-3D benchmarks. Meanwhile, the speed of our framework is significantly faster than other CNN-based approaches.