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Volume 13
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10.3390/diagnostics13071291
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Article

Implementation of an Attention Mechanism Model for Facial Beauty Assessment Using Transfer Learning

by
Chao-Tung Yang
1,2,
Yu-Chieh Wang
1,
Lun-Jou Lo
3,
Wen-Chung Chiang
4,
Shih-Ku Kuang
5 and
Hsiu-Hsia Lin
5,*
1
Department of Computer Science, Tunghai University, Taichung City 407224, Taiwan
2
Research Center for Smart Sustainable Circular Economy, Tunghai University, No. 1727, Sec. 4, Taiwan Boulevard, Taichung City 407224, Taiwan
3
Department of Plastic and Reconstructive Surgery, Craniofacial Research Center, Chang Gung Memorial Hospital, No. 123, Dinghu Rd., Guishan Township, Taoyuan City 333423, Taiwan
4
Department of Tourism and Recreation Management, Hsiuping University of Science and Technology, No. 11, Gongye Rd., Dali District, Taichung City 412406, Taiwan
5
Imaging Laboratory, Craniofacial Research Center, Chang Gung Memorial Hospital, No. 123, Dinghu Rd., Guishan Township, Taoyuan City 333423, Taiwan
*
Author to whom correspondence should be addressed.
Diagnostics 2023, 13(7), 1291; https://doi.org/10.3390/diagnostics13071291
Submission received: 16 December 2022 / Revised: 8 March 2023 / Accepted: 17 March 2023 / Published: 29 March 2023
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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Abstract

An important consideration in medical plastic surgery is the evaluation of the patient’s facial symmetry. However, because facial attractiveness is a slightly individualized cognitive experience, it is difficult to determine face attractiveness manually. This study aimed to train a model for assessing facial attractiveness using transfer learning while also using the fine-grained image model to separate similar images by first learning features. In this case, the system can make assessments based on the input of facial photos. Thus, doctors can quickly and objectively treat patients’ scoring and save time for scoring. The transfer learning was combined with CNN, Xception, and attention mechanism models for training, using the SCUT-FBP5500 dataset for pre-training and freezing the weights as the transfer learning model. Then, we trained the Chang Gung Memorial Hospital Taiwan dataset to train the model based on transfer learning. The evaluation uses the mean absolute error percentage (MAPE) value. The root mean square error (RMSE) value is used as the basis for experimental adjustment and the quantitative standard for the model’s predictive. The best model can obtain 0.50 in RMSE and 18.5% average error in MAPE. A web page was developed to infer the deep learning model to visualize the predictive model.
Keywords: transfer learning; deep learning; facial attractiveness prediction; attention mechanism; visualization transfer learning; deep learning; facial attractiveness prediction; attention mechanism; visualization

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MDPI and ACS Style

Yang, C.-T.; Wang, Y.-C.; Lo, L.-J.; Chiang, W.-C.; Kuang, S.-K.; Lin, H.-H. Implementation of an Attention Mechanism Model for Facial Beauty Assessment Using Transfer Learning. Diagnostics 2023, 13, 1291. https://doi.org/10.3390/diagnostics13071291

AMA Style

Yang C-T, Wang Y-C, Lo L-J, Chiang W-C, Kuang S-K, Lin H-H. Implementation of an Attention Mechanism Model for Facial Beauty Assessment Using Transfer Learning. Diagnostics. 2023; 13(7):1291. https://doi.org/10.3390/diagnostics13071291

Chicago/Turabian Style

Yang, Chao-Tung, Yu-Chieh Wang, Lun-Jou Lo, Wen-Chung Chiang, Shih-Ku Kuang, and Hsiu-Hsia Lin. 2023. "Implementation of an Attention Mechanism Model for Facial Beauty Assessment Using Transfer Learning" Diagnostics 13, no. 7: 1291. https://doi.org/10.3390/diagnostics13071291

APA Style

Yang, C.-T., Wang, Y.-C., Lo, L.-J., Chiang, W.-C., Kuang, S.-K., & Lin, H.-H. (2023). Implementation of an Attention Mechanism Model for Facial Beauty Assessment Using Transfer Learning. Diagnostics, 13(7), 1291. https://doi.org/10.3390/diagnostics13071291

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