Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (18): 150-156.DOI: 10.3778/j.issn.1002-8331.1907-0119

Previous Articles     Next Articles

Facial Expression Generative Adversarial Networks Based on Facial Action Coding System

HU Xiaorui, LIN Jingyi, LI Dong, ZHANG Yun   

  1. School of Automation, Guangdong University of Technology, Guangzhou 510000, China
  • Online:2020-09-15 Published:2020-09-10



  1. 广东工业大学 自动化学院,广州 510000


Using the vector containing facial expression information as the input conditional to guide the generation of high-authenticity facial images is one of the important research topics, but the commonly used eight expression labels are relatively single, in order to reflect the abundant micro-expression information, the facial expression generative adversarial networks based on Facial Action Coding System(FACS) is proposed, in which each facial muscle group is used as Action Units(AUs). The attention mechanism is integrated into the encoding and decoding generation module, the network focuses on the local area and makes pertinent changes. An objective function based on reconstruction loss, classification loss and attention smoothing loss of discriminant module is used. The experimental results on common BP4D face datasets show that this method can pay more effective attention to the corresponding region location of each action unit and use a single AU label to control the expression generation, and the continuous value of AU labels can control the expression amplitude. Compared with other methods, the details of facial expression images generated by this method are clearer and more authentic.

Key words: facial expression generation, generative adversarial networks, facial action coding system



关键词: 人脸表情生成, 生成对抗网络, 面部动作编码系统