公开(公告)号：US20220327308A1

公开(公告)日：2022-10-13

申请号：US17448926

申请日：2021-09-27

申请人： Chongqing University ,  University of Electronic Science and Technology of China ,  Dibi (Chongqing) Intelligent Technology Research Institute Co., Ltd. ,  Star Institute of Intelligent Systems

发明人： Yongduan Song ,  Feng Yang ,  Rui Li ,  Yiwen Zhang ,  Haoyuan Zhong ,  Jian Zhang ,  Shengtao Pan ,  Siyu Li ,  Zhengtao Yu

IPC分类号： G06K9/00 ,  G06K9/62 ,  G06N3/04

摘要： 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.

公开(公告)号：US11804074B2

公开(公告)日：2023-10-31

申请号：US17448926

申请日：2021-09-27

申请人： Chongqing University ,  University of Electronic Science and Technology of China ,  Dibi (Chongqing) Intelligent Technology Research Institute Co., Ltd. ,  Star Institute of Intelligent Systems

发明人： Yongduan Song ,  Feng Yang ,  Rui Li ,  Yiwen Zhang ,  Haoyuan Zhong ,  Jian Zhang ,  Shengtao Pan ,  Siyu Li ,  Zhengtao Yu

IPC分类号： G06V40/16 ,  G06N3/04 ,  G06F18/214

CPC分类号： G06V40/174 ,  G06F18/2148 ,  G06N3/04 ,  G06V40/169 ,  G06V40/172

摘要： 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.