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Comparison of deep learning models for facial attractiveness assessment on 3D photos
Jieqiong Hu1, Chunhong Wang1, Qinyuan Qu1
1Department of Orthodontics, The Affiliated Stomatological Hospital Of Nanjing Medical University, State Key Laboratory Cultivation Base of Research, Prevention and Treatment for Oral Diseases, Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, PR China.
Objectives:
Convolutional neural networks (CNNs) have demonstrated remarkable success in orthodontics. This study aimed to evaluate the accuracy and precision of several prominent CNN models for evaluating the facial attractiveness in Chinese orthodontic patients aged 6-18 years.
Materials And Methods:
A total of 1272 three-dimensional (3D) pretreatment photographs of patients were gathered. A panel of seven Chinese orthodontists assessed facial attractiveness using a visual analog scale. After conversion to two-dimensional RGB images, the data were fed into CNN models, including ResNet18, ResNet50, ResNet101, VGG-16, VGG-19, Inception-v3, MobileNet-v2, and DenseNet121. The performance of these CNNs was evaluated using root mean square error (RMSE), mean absolute error (MAE), Kendall's tau (τ), and Spearman's rank correlation coefficient (ρ).
Results:
DenseNet121 demonstrated the best, and MobileNet-v2 performed the worst in terms of prediction accuracy. ResNet18 required the shortest training time yet was still able to accurately predict facial attractiveness. In addition, VGG-19 was the largest and consumed the most time during training. DenseNet121 successfully achieved a balance between the model performance and the training cost and outperformed the other CNNs in terms of ranking correlation, validation loss, and validation MAE. Meanwhile, ResNet101 failed to achieve a performance improvement after an initial stage as the number of epochs increased.
Conclusions:
DenseNet121 exhibited the best performance in terms of prediction accuracy and ranking correlation. Furthermore, the trade-offs was also analyzed between model accuracy and training efficiency, offering insights into model selection under different computational constraints.
Clinical Significance:
The successful application of deep learning models helps evaluate facial attractiveness accurately and precisely.
