Abstract
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.
Highlights
The face contains a large amount of information such as identity, age, expression and ethnicity
This paper proposes using an ensemble of regression trees to annotate corresponding facial features
The blue and the yellow were the result of using original 3DCNN; the red and the black were the result with Multi-features Cooperative Deep Convolutional Network (MC-DCN) with SAVEE and MMI
Summary
The face contains a large amount of information such as identity, age, expression and ethnicity. The amount of information contained in facial expressions in the communication process is second only to language [1]. Facial expressions are subtle signals of the communication process. Ekman et al identified the six facial expressions (happiness, sadness, disgust, fear, angry and surprise) as basic facial expressions that are universal among human beings, while other researchers added neutral, which, together with the previous six emotions, constitutes seven basic emotions [2,3,4,5]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have