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Face acupoint recognition and localization based on geometric topological relationships.

Deng Yifan¹, Zhang Hongxing¹, H E Qiang¹

¹Intelligent Medical Engineering Research Center, School of Artificial Intelligence, Jianghan University, Wuhan 430056, China.

Journal of Traditional Chinese Medicine = Chung I Tsa Chih Ying Wen Pan

|June 28, 2026

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This study introduces a novel method for accurately identifying facial acupoints using geometric and topological relationships (GTRs). This approach enhances precision in acupuncture systems by mapping key facial points without extensive manual data.

Area of Science:

Computational anatomy
Medical imaging analysis
Acupuncture research

Background:

Accurate facial acupoint localization is crucial for effective acupuncture therapy.
Existing methods often rely on manual identification or large datasets, limiting efficiency and applicability.

Purpose of the Study:

To develop a robust method for precise facial acupoint identification and localization.
To leverage geometric and topological relationships (GTRs) for automated acupoint mapping.

Main Methods:

A facial acupoint localization framework utilizing Google MediaPipe to extract 478 facial key points.
Establishing GTRs between acupoints and facial landmarks.
Designing a rule-based algorithm for acupoint identification and localization.

Keywords:

MediaPipe acupoint recognition deep learning geometric topological relationships key point extraction

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Main Results:

Successful identification and localization of facial acupoints based on stable GTRs.
Consistent performance across diverse facial structures.
Demonstrated feasibility and reliability for practical applications without large annotated datasets.

Conclusions:

The GTR-based approach offers an effective and efficient solution for facial acupoint localization.
Reduces reliance on manual annotation, improving clinical applicability.
Enhances intelligent acupuncture systems through automated and accurate acupoint mapping.