The Association between Facial Morphology and Cold Pattern

Ahn, Ilkoo; Bae, Kwang-Ho; Jin, Hee-Jeong; Lee, Si-Woo

doi:10.13048/jkm.21040

Cited by 1 publication

(4 citation statements)

References 33 publications

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“…In general, features that can be extracted from images include color, shape, and texture 28) . Among these features, the features used in existing cold pattern classification or analysis studies are facial color 23) and facial shape 24) . In the experiments conducted in this paper, additional research is needed to determine whether color and shape are…”

Section: Classification Results Of Low Cps and High Cpsmentioning

confidence: 99%

“…It is known that cold sensitivity varies depending on sex 9,10,23,24) . Therefore, additional experiments were conducted to classify the low CPS group and the high CPS group for male and female, respectively.…”

Section: Cold Patternmentioning

confidence: 99%

“…Existing studies [22][23][24] report that complexion or facial characteristics are related to cold sensitivity.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, Mun et al 23) reported that individuals with increased cold patterns tended to have pallor across the face. Ahn et al 24) reported that people with higher cold sensitivity tend to have a more pointed chin, longer face, more angular jaw, higher eyes, and more upward corners of the mouth.…”

Section: Introductionmentioning

confidence: 99%

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Cold sensitivity classification using facial image based on convolutional neural network

Ahn,

Baek,

Bae

et al. 2023

J Korean Med

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Objectives: Facial diagnosis is an important part of clinical diagnosis in traditional East Asian Medicine. In this paper, we proposed a model to quantitatively classify cold sensitivity using a fully automated facial image analysis system.Methods: We investigated cold sensitivity in 452 subjects. Cold sensitivity was determined using a questionnaire and the Cold Pattern Score (CPS) was used for analysis. Subjects with a CPS score below the first quartile (low CPS group) belonged to the cold non-sensitivity group, and subjects with a CPS score above the third quartile (high CPS group) belonged to the cold sensitivity group. After splitting the facial images into train/validation/test sets, the train and validation set were input into a convolutional neural network to learn the model, and then the classification accuracy was calculated for the test set.Results: The classification accuracy of the low CPS group and high CPS group using facial images in all subjects was 76.17%. The classification accuracy by sex was 69.91% for female and 62.86% for male. It is presumed that the deep learning model used facial color or facial shape to classify the low CPS group and the high CPS group, but it is difficult to specifically determine which feature was more important.Conclusions: The experimental results of this study showed that the low CPS group and the high CPS group can be classified with a modest level of accuracy using only facial images. There was a need to develop more advanced models to increase classification accuracy.

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Section: Classification Results Of Low Cps and High Cpsmentioning

confidence: 99%