University of Electronic Science and Technology of China

Chongqing University

Star Institute of Intelligent Systems

Dibi (chongqing) Intelligent Technology Research Institute Co., Ltd.

Haoyuan Zhong

Yongduan Song

Jian Zhang

Rui Li

Feng Yang

Zhengtao Yu

Siyu Li

Shengtao Pan

Yiwen Zhang

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.

Method for recognizing facial expressions based on adversarial elimination

Growing community of inventors

Chongqing, China

Haoyuan Zhong