DLSANet: Facial expression recognition with double‐code LBP‐layer spatial‐attention network
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Published:2023-04-24
Issue:9
Volume:17
Page:2659-2672
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ISSN:1751-9659
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Container-title:IET Image Processing
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language:en
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Short-container-title:IET Image Processing
Author:
Guo Xing1,
Lu Siyuan2,
Wang Shuihua324,
Lu Zhihai1,
Zhang Yudong324ORCID
Affiliation:
1. School of Educational Science Nanjing Normal University Nanjing Jiangsu China
2. School of Computer Science and Technology Henan Polytechnic University Jiaozuo Henan People's Republic of China
3. School of Computing and Mathematical Sciences University of Leicester Leicester UK
4. Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia
Abstract
AbstractFacial expression recognition (FER) is widely used in many fields. To further improve the accuracy of FER, this paper proposes a method based on double‐code LBP‐layer spatial‐attention network (DLSANet). The backbone model for the DLSANet is an emotion network (ENet), which is modified with a double‐code LBP (DLBP) layer and a spatial attention module. The DLBP layer is at the front of the first convolutional layer. More valuable features can be extracted by inputting the image processed by DLBP into convolutional layers. The JAFFE and CK+ datasets are used, which contain seven expressions: happiness, anger, disgust, neutral, fear, sadness, and surprise. The average of fivefold cross‐validation shows that DLSANet achieves a recognition accuracy of 93.81% and 98.68% on the JAFFE and CK+ datasets. The experiment reveals that the DLSANet can produce better classification results than state‐of‐the‐art methods.
Funder
Priority Academic Program Development of Jiangsu Higher Education Institutions
Medical Research Council
Royal Society
British Heart Foundation
Fight for Sight
Biotechnology and Biological Sciences Research Council
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software
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