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KMID : 0878220220340030031
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Journal of Sasang Constitutional Medicine
2022 Volume.34 No. 3 p.31 ~ p.40
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Sasang Constitution Classification using Convolutional Neural Network on Facial Images
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Ahn Il-Koo
Kim Sang-Hyuk
Jeong Kyoung-Sik
Kim Ho-Seok
Lee Si-Woo
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Abstract
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Objectives Sasang constitutional medicine is a traditional Korean medicine that classifies humans into four constitutions in consideration of individual differences in physical, psychological, and physiological characteristics. In this paper, we proposed a method to classify Taeeum person (TE) and Non-Taeeum person (NTE), Soeum person (SE) and Non-Soeum person (NSE), and Soyang person (ST) and Non-Soyang person (NSY) using a convolutional neural network with only facial images.
Methods Based on the convolutional neural network VGG16 architecture, transfer learning is carried out on the facial images of 3738 subjects to classify TE and NTE, SE and NSE, and SY and NSY. Data augmentation techniques are used to increase classification performance.
Results The classification performance of TE and NTE, SE and NSE, and SY and NSY was 77.24%, 85.17%, and 80.18% by F1 score and 80.02%, 85.96%, and 72.76% by Precision-Recall AUC (Area Under the receiver operating characteristic Curve) respectively.
Conclusions It was found that Soeum person had the most heterogeneous facial features as it had the best classification performance compared to the rest of the constitution, followed by Taeeum person and Soyang person. The experimental results showed that there is a possibility to classify constitutions only with facial images. The performance is expected to increase with additional data such as BMI or personality questionnaire.
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KEYWORD
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Sasang Constitution, Facial Images, Convolutional Neural Network, Deep Learning, Transfer Learning
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