EfficientFAN: Deep knowledge transfer for face alignment

Abstract

Face alignment plays an important role in many applications that process facial images. At present, deep learning-based methods have achieved excellent results in face alignment. However, these models usually have a large number of parameters, resulting in high computational complexity and execution time. In this paper, a lightweight, efficient, and effective model is proposed and named Efficient Face Alignment Network (EfficientFAN). EfficientFAN adopts the encoder-decoder structure, using a simple backbone Efficient-Net-B0 as the encoder and three deconvolutional layers as the decoder. Compared with state-of-the-art models, it achieves equivalent performance with fewer model parameters, lower computation cost, and higher speed. Moreover, the accuracy of EfficientFAN is further improved by transferring deep knowledge of a complex teacher network through feature-aligned distillation and patch similarity distillation. Extensive experimental results on public data sets demonstrate the superiority of EfficientFAN over state-of-the-art methods.