Method for recognizing facial expressions based on adversarial elimination

US11804074B2 · kind B2 · utility

Assignees

• CHONGQING UNIVERSITY · CN
• University of Electronic Science and Technology of China · CN
• Dibi (Chongqing) Intelligent Technology Research Institute Co., Ltd. · CN
• Star Institute of Intelligent Systems · CN

Inventors

• Yongduan Song · Chongqing, CN
• Feng Yang · Chengdu, CN
• Rui Li · Los Angeles, US
• Yiwen Zhang · Chongqing, CN
• Haoyuan Zhong · Chongqing, CN
• Jian Zhang · Jinan, CN
• Shengtao Pan · Chongqing, CN
• Siyu Li · Guangyuan, CN
• Zhengtao Yu · Mountain View, US

Key dates

• Filing date: Sep 27, 2021
• Grant date: Oct 31, 2023
• Expiry date: Jul 7, 2042

Classification

• Technology area (CPC G): Physics
• CPC primary: G06V40/172
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

The present disclosure relates to a method for recognizing facial expressions based on adversarial elimination. First, a facial expression recognition network is built based on a deep convolutional neural network. On a natural facial expression data set, the facial expression recognition network is trained through a loss function to make facial expression features easier to distinguish. Then some key features of input images are actively eliminated by using an improved confrontation elimination method to generate a new data set to train new networks with different weight distributions and feature extraction capabilities, forcing the network to perform expression classification discrimination based on more features, which reduces the influence of interference factors such as occlusion on the network recognition accuracy rate, and improving the robustness of the facial expression recognition network. Finally, the final expression classification predicted results are obtained by using network integration and a relative majority voting method.