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Real‐time facial expression recognition based on iterative transfer learning and efficient attention network
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Kong, Yinghui, Zhang, Shuaitong, Zhang, Ke, Ni, Qiang and Han, Jungong (2022) Real‐time facial expression recognition based on iterative transfer learning and efficient attention network. IET Image Processing, 16 (6). pp. 1694-1708. doi:10.1049/ipr2.12441 ISSN 1751-9659.
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WRAP-real‐time-facial-expression-recognition-based-iterative-transfer-learning-efficient-attention-network-Han-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (4Mb) | Preview |
Official URL: https://doi.org/10.1049/ipr2.12441
Abstract
Real‐time facial expression recognition is the basis for computers to understand human emotions and detect abnormalities in time. To effectively solve the problems of server overload and privacy information leakage, a real‐time facial expression recognition method based on iterative transfer learning and efficient attention network (EAN) for edge resource‐constrained scenes is proposed in this paper. Firstly, an EAN is designed with its parameter number and computation amount strictly limited by depth separable convolution and local channel attention mechanism. Then, the soft labels of facial expression data were obtained by EAN based on the idea of knowledge distillation, so as to provide more supervision information for the training process. Finally, an iterative transfer learning method of teacher‐student (T‐S) network was proposed; it refines the soft labels of the teacher network and further improves the recognition accuracy of the student network. The tests on the public datasets, FER2013 and RAF‐DB, show that this method can significantly reduce the model complexity and achieve high recognition accuracy. Compared with other advanced methods, the proposed method strikes a good balance between complexity and accuracy, and well meets the real‐time deployment requirements of facial expression recognition technology for edge resource‐constrained scenes.
| Item Type: | Journal Article | ||||||||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QP Physiology T Technology > TA Engineering (General). Civil engineering (General) |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||||||||||||
| SWORD Depositor: | Library Publications Router | ||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Computer vision , Human-computer interaction , Optical pattern recognition , Image processing -- Digital techniques , Image analysis -- Data processing, Pattern recognition systems , Human face recognition (Computer science), Facial expression, Emotion recognition | ||||||||||||||||||
| Journal or Publication Title: | IET Image Processing | ||||||||||||||||||
| Publisher: | The Institution of Engineering and Technology | ||||||||||||||||||
| ISSN: | 1751-9659 | ||||||||||||||||||
| Official Date: | May 2022 | ||||||||||||||||||
| Dates: |
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| Volume: | 16 | ||||||||||||||||||
| Number: | 6 | ||||||||||||||||||
| Page Range: | pp. 1694-1708 | ||||||||||||||||||
| DOI: | 10.1049/ipr2.12441 | ||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||||||||
| Date of first compliant deposit: | 8 March 2022 | ||||||||||||||||||
| Date of first compliant Open Access: | 9 March 2022 | ||||||||||||||||||
| RIOXX Funder/Project Grant: |
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