A CPU Real-Time Face Alignment for Mobile Platform

Face alignment is a common technology in face recognition and face verification field. Previous works mostly pay attention to improving the accuracy of prediction and ignored the practicability of the method. In this paper, we aim at providing a two-stage face alignment network for mobile platform. Firstly, the network was trained with residual label which is the difference between ground truth and mean shape. Secondly, the input data in the second stage is composed of the original data and generated heatmap which enriched the data types. Finally, a new loss function is used to enhance the convergence of local region. Experimental results show that our method not only provides high precision but also improve the real-time processing performance on the mobile platforms.