An image‐based facial acupoint detection approach using high‐resolution network and attention fusion
Abstract With the prevalence of Traditional Chinese Medicine (TCM), automation techniques are highly required to support the therapy and save human resources. As the fundamental of the TCM treatment, acupoint detection is attracting research attention in both academic and industrial domains, while c...
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| Format: | Article |
| Language: | English |
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Wiley
2023-05-01
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| Series: | IET Biometrics |
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| Online Access: | https://doi.org/10.1049/bme2.12113 |
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| _version_ | 1850106814537924608 |
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| author | Tingting Zhang Hongyu Yang Wenyi Ge Yi Lin |
| author_facet | Tingting Zhang Hongyu Yang Wenyi Ge Yi Lin |
| author_sort | Tingting Zhang |
| collection | DOAJ |
| description | Abstract With the prevalence of Traditional Chinese Medicine (TCM), automation techniques are highly required to support the therapy and save human resources. As the fundamental of the TCM treatment, acupoint detection is attracting research attention in both academic and industrial domains, while current approaches suffer from poor accuracy even with sparse acupoints or require extra equipment. In this study, considering the decision‐making knowledge of human experts, an image‐based deep learning approach is proposed to detect facial acupoints by localising the centre of acupoints. In the proposed approach, high‐resolution networks are selected as the backbone to learn informative facial features with different resolution paths. To fuse the learnt features from the high‐resolution network, a resolution, channel, and spatial attention‐based fusion module is innovatively proposed to imitate human decision, that is, focusing on the facial features to detect required acupoints. Finally, the heatmap is designed to integrally achieve the acupoint classification and position localisation in a single step. A small‐scale real‐world dataset is constructed and annotated to evaluate the proposed approach based on the authorised face dataset. The experimental results demonstrate the proposed approach outperforms other baseline models, achieving a 2.4228% normalised mean error. Most importantly, the effectiveness and efficiency of the proposed technical improvements are also confirmed by extensive experiments. The authors believe that the proposed approach can achieve acupoint detection with considerable high performance, and further support TCM automation. |
| format | Article |
| id | doaj-art-4ddffb6859a7450781c4ddccd63e9ba9 |
| institution | OA Journals |
| issn | 2047-4938 2047-4946 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-4ddffb6859a7450781c4ddccd63e9ba92025-08-20T02:38:45ZengWileyIET Biometrics2047-49382047-49462023-05-0112314615810.1049/bme2.12113An image‐based facial acupoint detection approach using high‐resolution network and attention fusionTingting Zhang0Hongyu Yang1Wenyi Ge2Yi Lin3College of Computer Science Sichuan University Chengdu ChinaCollege of Computer Science Sichuan University Chengdu ChinaCollege of Computer Science Chengdu University of Information Technology Chengdu ChinaCollege of Computer Science Sichuan University Chengdu ChinaAbstract With the prevalence of Traditional Chinese Medicine (TCM), automation techniques are highly required to support the therapy and save human resources. As the fundamental of the TCM treatment, acupoint detection is attracting research attention in both academic and industrial domains, while current approaches suffer from poor accuracy even with sparse acupoints or require extra equipment. In this study, considering the decision‐making knowledge of human experts, an image‐based deep learning approach is proposed to detect facial acupoints by localising the centre of acupoints. In the proposed approach, high‐resolution networks are selected as the backbone to learn informative facial features with different resolution paths. To fuse the learnt features from the high‐resolution network, a resolution, channel, and spatial attention‐based fusion module is innovatively proposed to imitate human decision, that is, focusing on the facial features to detect required acupoints. Finally, the heatmap is designed to integrally achieve the acupoint classification and position localisation in a single step. A small‐scale real‐world dataset is constructed and annotated to evaluate the proposed approach based on the authorised face dataset. The experimental results demonstrate the proposed approach outperforms other baseline models, achieving a 2.4228% normalised mean error. Most importantly, the effectiveness and efficiency of the proposed technical improvements are also confirmed by extensive experiments. The authors believe that the proposed approach can achieve acupoint detection with considerable high performance, and further support TCM automation.https://doi.org/10.1049/bme2.12113biometric applicationsface biometrics |
| spellingShingle | Tingting Zhang Hongyu Yang Wenyi Ge Yi Lin An image‐based facial acupoint detection approach using high‐resolution network and attention fusion IET Biometrics biometric applications face biometrics |
| title | An image‐based facial acupoint detection approach using high‐resolution network and attention fusion |
| title_full | An image‐based facial acupoint detection approach using high‐resolution network and attention fusion |
| title_fullStr | An image‐based facial acupoint detection approach using high‐resolution network and attention fusion |
| title_full_unstemmed | An image‐based facial acupoint detection approach using high‐resolution network and attention fusion |
| title_short | An image‐based facial acupoint detection approach using high‐resolution network and attention fusion |
| title_sort | image based facial acupoint detection approach using high resolution network and attention fusion |
| topic | biometric applications face biometrics |
| url | https://doi.org/10.1049/bme2.12113 |
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