Facial Expression Recognition Based on Multi-Features Cooperative Deep Convolutional Network

Applied Sciences

卷:11

Haopeng Wu¹  Zhiying Lu¹  Mingyue Zhao¹  Xin Li¹  Xudong Ding¹  Jianfeng Zhang² 

[1] School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China;

[2] School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China;

关键词: classification;    deep learning;    facial expression recognition;    3D convolutional neural networks;    sequence correlation processing;    target location;   

DOI  :  10.3390/app11041428

来源: DOAJ

【 摘 要 】

This paper addresses the problem of Facial Expression Recognition (FER), focusing on unobvious facial movements. Traditional methods often cause overfitting problems or incomplete information due to insufficient data and manual selection of features. Instead, our proposed network, which is called the Multi-features Cooperative Deep Convolutional Network (MC-DCN), maintains focus on the overall feature of the face and the trend of key parts. The processing of video data is the first stage. The method of ensemble of regression trees (ERT) is used to obtain the overall contour of the face. Then, the attention model is used to pick up the parts of face that are more susceptible to expressions. Under the combined effect of these two methods, the image which can be called a local feature map is obtained. After that, the video data are sent to MC-DCN, containing parallel sub-networks. While the overall spatiotemporal characteristics of facial expressions are obtained through the sequence of images, the selection of keys parts can better learn the changes in facial expressions brought about by subtle facial movements. By combining local features and global features, the proposed method can acquire more information, leading to better performance. The experimental results show that MC-DCN can achieve recognition rates of 95%, 78.6% and 78.3% on the three datasets SAVEE, MMI, and edited GEMEP, respectively.

【 授权许可】

Unknown