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Mask suitability recommendation based on facial image key feature dimension recognition
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Xinjin YANG1, Weiqun XIE1, Zhenyu HUANG1, Yixiong SHEN1, Lin YE1, Hai LI2, Zhuobo YANG2, Zhu LIAO2, Simi LI3
Chinese Journal of Public Health | 2025, 41(3) : 360 - 365
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Chinese Journal of Public Health | 2025, 41(3): 360-365
• Survey and Report •
Mask suitability recommendation based on facial image key feature dimension recognition
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Xinjin YANG1, Weiqun XIE1, Zhenyu HUANG1, Yixiong SHEN1, Lin YE1, Hai LI2, Zhuobo YANG2, Zhu LIAO2, Simi LI3
Affiliations
  • 1Center for Disease Control and Prevention of Dongguan City, Guangdong Province, Dongguan 523000, China
  • 2South China University of Technology, Guangzhou 510000, China
  • 3Guangzhou TerraTrust Tech Service Co., Ltd., Guangzhou 510000, China
Published: 2025-03-10 doi: 10.11847/zgggws1144333
Outline
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Objective

To develop a mask suitability correlation model based on facial features extracted from facial images using facial recognition technology, enabling rapid mask recommendations for users and reducing infection risk.

Methods

Frontal facial photographs of volunteers were collected, and mask sealing performance tests were conducted to obtain facial feature dimensions and mask suitability data. Dimensionality reduction and clustering were applied to the facial feature data, and a correlation model was established between facial clusters and mask sealing performance data. This model facilitates rapid mask recommendations based on facial images.

Results

Facial image data from 245 volunteers were collected. Each volunteer randomly selected 2–3 masks from five common types for sealing performance tests, resulting in a total of 589 mask sealing performance data points, with each mask type tested 115–125 times. Statistical analysis revealed that two masks, ENVIP3DF (84.3%) and 801-N95 (68.9%), had higher passing rates, with a statistically significant difference (χ2=127.546, P<0.05). The majority of the data was used for model training, and a small portion was used for validation. Validation results showed that for volunteer 1, the test scores and recommendation degrees for two randomly selected masks were ENVIP3DF (200, 95.0%) and 801-N95 (145, 94.6%), respectively; for volunteer 5, the scores and recommendation degrees were 801-N95 (152, 64.7%) and 9132 (52, 39.4%), respectively. For each volunteer, there was a significant positive correlation between the actual mask sealing performance test score and the proposed mask suitability recommendation correlation model (r=0.642, P<0.05). The proposed method can accurately predict mask sealing test results based on facial image processing data.

Conclusions

This method can rapidly recommend optimal masks based on facial images and is suitable for emergencies or situations where standard mask sealing performance testing is inconvenient.

mask sealing performance  /  facial key feature dimensions  /  mask suitability recommendation
杨新金, 谢伟群, 黄振宇, 沈逸雄, 叶霖, 李海, 杨倬波, 廖祝, 李思米. 基于人脸图像关键特征尺寸识别的口罩适用性推荐研究. 中国公共卫生, 2025 , 41 (3) : 360 -365 . DOI: 10.11847/zgggws1144333
Xinjin YANG, Weiqun XIE, Zhenyu HUANG, Yixiong SHEN, Lin YE, Hai LI, Zhuobo YANG, Zhu LIAO, Simi LI. Mask suitability recommendation based on facial image key feature dimension recognition[J]. Chinese Journal of Public Health, 2025 , 41 (3) : 360 -365 . DOI: 10.11847/zgggws1144333
Year 2025 volume 41 Issue 3
67
27
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Article Info
doi: 10.11847/zgggws1144333
  • Receive Date:2024-03-04
  • Online Date:2026-10-25
  • Published:2025-03-10
Article Data
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History
  • Received:2024-03-04
  • Revised:2024-09-20
  • Accepted:2024-11-27
Affiliations
    1Center for Disease Control and Prevention of Dongguan City, Guangdong Province, Dongguan 523000, China
    2South China University of Technology, Guangzhou 510000, China
    3Guangzhou TerraTrust Tech Service Co., Ltd., Guangzhou 510000, China

Corresponding:

LIAO Zhu, E-mail:
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小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
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