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

Chao Tung Yang, Yu Chieh Wang, Lun Jou Lo, Wen Chung Chiang, Shih Ku Kuang, Hsiu Hsia Lin*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

2 Scopus citations


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.

Original languageEnglish
Article number1291
Issue number7
StatePublished - 29 03 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.


  • attention mechanism
  • deep learning
  • facial attractiveness prediction
  • transfer learning
  • visualization


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