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Facial expression recognition method based on PSA—YOLO network
In order to improve the recognition speed and accuracy of face expression recognition, we propose a face expression recognition method based on PSA—YOLO (Pyramids Squeeze Attention—You Only Look Once). Based on CSPDarknet53, the Focus structure and pyramid compression channel attention mechanism are...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9887114/ https://www.ncbi.nlm.nih.gov/pubmed/36733905 http://dx.doi.org/10.3389/fnbot.2022.1057983 |
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author | Ma, Ruoling Zhang, Ruoyuan |
author_facet | Ma, Ruoling Zhang, Ruoyuan |
author_sort | Ma, Ruoling |
collection | PubMed |
description | In order to improve the recognition speed and accuracy of face expression recognition, we propose a face expression recognition method based on PSA—YOLO (Pyramids Squeeze Attention—You Only Look Once). Based on CSPDarknet53, the Focus structure and pyramid compression channel attention mechanism are integrated, and the network depth reduction strategy is adopted to build a PSA-CSPDarknet-1 lightweight backbone network with small parameters and high accuracy, which improves the speed of face expression recognition. Secondly, in the neck of the network, a spatial pyramid convolutional pooling module is built, which enhances the spatial information extraction ability of deep feature maps with a very small computational cost, and uses the α—CIoU loss function as the bounding box loss function to improve the recognition accuracy of the network for targets under high IoU threshold and improve the accuracy of face expression recognition. The proposed method is validated on the JAFFE, CK+, and Cohn-Kanade datasets. The experimental results show that the running time of the proposed method and the comparison method is reduced from 1,800 to 200 ms, and the recognition accuracy is increased by 3.11, 2.58, and 3.91%, respectively, so the method proposed in this paper has good applicability. |
format | Online Article Text |
id | pubmed-9887114 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98871142023-02-01 Facial expression recognition method based on PSA—YOLO network Ma, Ruoling Zhang, Ruoyuan Front Neurorobot Neuroscience In order to improve the recognition speed and accuracy of face expression recognition, we propose a face expression recognition method based on PSA—YOLO (Pyramids Squeeze Attention—You Only Look Once). Based on CSPDarknet53, the Focus structure and pyramid compression channel attention mechanism are integrated, and the network depth reduction strategy is adopted to build a PSA-CSPDarknet-1 lightweight backbone network with small parameters and high accuracy, which improves the speed of face expression recognition. Secondly, in the neck of the network, a spatial pyramid convolutional pooling module is built, which enhances the spatial information extraction ability of deep feature maps with a very small computational cost, and uses the α—CIoU loss function as the bounding box loss function to improve the recognition accuracy of the network for targets under high IoU threshold and improve the accuracy of face expression recognition. The proposed method is validated on the JAFFE, CK+, and Cohn-Kanade datasets. The experimental results show that the running time of the proposed method and the comparison method is reduced from 1,800 to 200 ms, and the recognition accuracy is increased by 3.11, 2.58, and 3.91%, respectively, so the method proposed in this paper has good applicability. Frontiers Media S.A. 2023-01-17 /pmc/articles/PMC9887114/ /pubmed/36733905 http://dx.doi.org/10.3389/fnbot.2022.1057983 Text en Copyright © 2023 Ma and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Ma, Ruoling Zhang, Ruoyuan Facial expression recognition method based on PSA—YOLO network |
title | Facial expression recognition method based on PSA—YOLO network |
title_full | Facial expression recognition method based on PSA—YOLO network |
title_fullStr | Facial expression recognition method based on PSA—YOLO network |
title_full_unstemmed | Facial expression recognition method based on PSA—YOLO network |
title_short | Facial expression recognition method based on PSA—YOLO network |
title_sort | facial expression recognition method based on psa—yolo network |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9887114/ https://www.ncbi.nlm.nih.gov/pubmed/36733905 http://dx.doi.org/10.3389/fnbot.2022.1057983 |
work_keys_str_mv | AT maruoling facialexpressionrecognitionmethodbasedonpsayolonetwork AT zhangruoyuan facialexpressionrecognitionmethodbasedonpsayolonetwork |