Advanced Search

Journal of Traditional Chinese Medicine >

2026 , Vol. 46 >Issue 3: 733 - 739

DOI: https://doi.org/10.19852/j.cnki.jtcm.2026.03.013

Original Articles

Face acupoint recognition and localization based on geometric topological relationships

  • DENG Yifan ,
  • ZHANG Hongxing ,
  • HE Qiang
Expand
  • Intelligent Medical Engineering Research Center, School of Artificial Intelligence, Jianghan University, Wuhan 430056, China
HE Qiang, Intelligent Medical Engineering Research Center, School of Artificial Intelligence, Jianghan University, Wuhan 430056, China. qh2020@jhun.edu.cn, Telephone: +86-27-84736508

Received date: 2025-01-12

  Accepted date: 2025-09-05

  Online published: 2026-06-08

Supported by

Research on Intelligent Medical Diagnosis and Treatment under the Major Innovation Support Program of Jianghan University(2023ZDCX02)

Fold

Abstract

OBJECTIVE: To develop a method for accurate identification and localization of facial acupoints based on geometric and topological relationships (GTRs) among facial key points and organ features.

METHODS: A facial acupoint localization framework was constructed using the Google MediaPipe machine learning toolkit to extract 478 facial key points. Geometric and topological relationships between predefined acupoints and key facial landmarks were established. Based on these relationships, a rule-based mapping algorithm was designed to identify and localize facial acupoints. The method was applied to facial images collected from individuals undergoing acupuncture and physical therapy, and its localization performance was evaluated.

RESULTS: The proposed method successfully identified and localized facial acupoints based on stable geometric and topological relationships. The approach demonstrated consistent performance across different facial structures, enabling accurate positioning of acupoints without the need for large-scale annotated datasets. The results indicate that the method is feasible and reliable for practical applications.

CONCLUSION: The GTR-based approach provides an effective and efficient solution for facial acupoint identification and localization, reducing dependence on manual annotation and improving applicability in clinical and intelligent acupuncture systems.

Key words： acupoint recognition; MediaPipe; geometric topological relationships; key point extraction; deep learning

Cite this article

DENG Yifan , ZHANG Hongxing , HE Qiang . Face acupoint recognition and localization based on geometric topological relationships[J]. Journal of Traditional Chinese Medicine, 2026 , 46(3) : 733 -739 . DOI: 10.19852/j.cnki.jtcm.2026.03.013

References

1. Godson DR, Wardle JL. Accuracy and precision in acupuncture point location: a critical systematic review. J Acupunct Meridian Stud 2019; 12: 52-66.
2. Yang ES, Li PW, Nilius B, Li G. Ancient Chinese medicine and mechanistic evidence of acupuncture physiology. Pflugers Arch 2011; 462: 645-53.
3. Li HR, Ma J, Yin YJ, Chang LP, Gao ML, Wei C. Advances in anti-aging research on traditional Chinese medicine characteristic therapies. Shi Jie Zhong Yi Yao 2023; 18: 3608-15.
4. Ran W, Gao S. Advances in treatment of facial rejuvenation in the past ten years. Zhong Guo Xiu Fu Chong Jian Wai Ke Za Zhi 2018; 32: 809-14.
5. Wang H, Yin N, Wang A, Xu G. Cortical functional networks of transcutaneous electrical stimulation at acupoints on the pericardial meridian. Neuropsychologia 2023; 189: 108669.
6. Kim DH, Ryu Y, Hahm DH, Sohn BY, Shim I, Kwon OS. Acupuncture points can be identified as cutaneous neurogenic inflammatory spots. Sci Rep 2017; 7: 15214.
7. Lan KC, Litscher G. Robot-controlled acupuncture: an innovative step towards modernization of the ancient traditional medical treatment method. Medicines (Basel) 2019; 6: 87.
8. Xu TC, Xia YB. Development of intelligent medical devices and the internationalization of acupuncture: reflections from an acupuncture robot researcher. Zhong Guo Zhen Jiu 2022; 42: 199-202.
9. Zhong LD. Applications of X-ray technology in medicine. Ke Ji Feng 2019; 88.
10. Bongratz F, Rickmann AM, P?lsterl S, Wachinger C. Vox2cortex: fast explicit reconstruction of cortical surfaces from 3D MRI scans with geometric deep neural networks. Proc IEEE Conf Comput Vis Pattern Recognit 2022: 20773-83.
11. Gao HY. Exploration of the application of CT three-dimensional reconstruction technology in medical imaging diagnosis. Zhong Wen Ke Ji Qi Kan Shu Ju Ku Yi Yao Wei Sheng 2022: 216-8.
12. Wei Y, Ma XY, Gao ZY. Research on human acupoint recognition based on convolutional neural networks. Zhong Yi Yao Xin Xi 2024; 41: 39-43.
13. Zhang TT, Yang HY, Yi L. A traditional Chinese medicine facial acupoint detection framework integrating representation learning. Dian Zi Ke Ji Da Xue Xue Bao 2023; 52: 175.
14. Kuang RX. Recognition and localization of human back acupoints based on deep learning. Nanchang: Nanchang University; 2022: 1-120.
15. Wang C. A vision-based acupoint localization method for laser acupuncture robots. Beijing: Beijing University of Posts and Telecommunications; 2020: 1-110.
16. Kerautret Y, Di Rienzo F, Eyssautier C, Guillot A. Comparative efficacy of robotic and manual massage interventions on performance and well-being: a randomized crossover trial. Sports Health 2024; 16: 650-60.
17. Guertin-Lahoud S, Coursaris CK, Sénécal S, et al. User experience evaluation in shared interactive virtual reality. Cyberpsychol Behav Soc Netw 2023; 26: 263-72.
18. Jiang Y, Li F, Li Z, Liu Z, Wang Z. and Automation ICA; 2023 Aug 4-6; Bandung, Enhancing continuous sign language recognition with self-attention and MediaPipe Holistic. 2023 International Conference on Instrumentation, Control, Indonesia. Piscataway (NJ): IEEE; 2023: 97-102.
19. Tavira JV, Quintero JO de JT, Salazar AM, Elías RP. Comparison of tools to locate occluded faces. J Eng Res 2023; 3: 1-10.
20. Siam AI, Soliman NF, Algarni AD, et al. Deploying machine learning techniques for human emotion detection. Comput Intell Neurosci 2022; 2022: 8032673.
21. Zhang F, Bazarevsky V, Vakunov A, et al. MediaPipe Hands: On-device real-time hand tracking. In: Proceedings of the CVPR Workshop on Computer Vision for Augmented and Virtual Reality (CV4ARVR); 2020 Jun; Seattle, WA, USA. 2020.
22. Kocabas M, Athanasiou N, Black MJ. VIBE: Video inference for human body pose and shape estimation. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2020 Jun 13-19; Seattle, WA, USA. Piscataway (NJ): IEEE; 2020: 5252-62.
23. Belov AG. A mathematical-statistics approach to the least squares method. Comput Math Model 2018; 29: 30-41.
24. Ke Z, Yu TT, Shi CJ, et al. Face age synthesis fusing channel-coordinate attention mechanism and parallel dilated convolution. J Image Graph 2023; 28: 3870-83.
25. Zhang T, Yang H, Ge W, Lin Y. An image-based facial acupoint detection approach using high-resolution network and attention fusion. IET Biometrics 2023; 12: 146-58.
26. Masood D, Qi J. 3D localization of hand acupoints using hand geometry and landmark points based on RGB-D CNN fusion. Ann Biomed Eng 2022; 50: 1103-15.
Options
Outlines

/

〈 〉