The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Oct. 31, 2023
Filed:
Sep. 27, 2021
Chongqing University, Chongqing, CN;
University of Electronic Science and Technology of China, Chengdu, CN;
Dibi (Chongqing) Intelligent Technology Research Institute Co., Ltd., Chongqing, CN;
Star Institute of Intelligent Systems, Chongqing, CN;
Yongduan Song, Chongqing, CN;
Feng Yang, Chongqing, CN;
Rui Li, Chongqing, CN;
Yiwen Zhang, Chongqing, CN;
Haoyuan Zhong, Chongqing, CN;
Jian Zhang, Chongqing, CN;
Shengtao Pan, Chongqing, CN;
Siyu Li, Chongqing, CN;
Zhengtao Yu, Chongqing, CN;
Chongqing University, Chongqing, CN;
University of Electronic Science and Technology of China, Chengdu, CN;
Dibi (Chongqing) Intelligent Technology Research Institute Co., Ltd., Chongqing, CN;
Star Institute of Intelligent Systems, Chongqing, CN;
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.