Microexpression to Macroexpression: Facial Expression Magnification by Single Input

Yaqi Song, Tong Chen, Shigang Li, Jianfeng Li

Abstract: Microexpressions are expressions that people inadvertently express, and therefore often represent a person’s true emotion. However, because it has a low intensity and a short duration, it is hard to be recognized correctly. In this paper, we propose a deep learning magnification method to generate macroexpressions from a single microexpression image. In the first stage, we extract the expression information from a single microexpression image. Then, We combine the idea of cyclegan and optical flow consistency to model the extracted expression features as the optical flow field between the neutral face and microexpressions. To extract a reliable optical flow field from the expression information, we design an optical flow refiner. In the second stage, we adopt an encoder-decoder network and let it learn to magnify the optical flow. Finally, the magnified optical flow guided the microexpression images to generate macroexpression images. We compare our single input based network with current two-frames-input based networks. The results show that our method performs better, even in wild images. We fed our magnified images directly into a simple ResNet18 network for recognition, achieving a competitive score under the MEGC2019 standard, compared with recent complex recognition networks.