Facial Beauty Prediction Using an Ensemble of Deep Convolutional Neural Networks

The topic of facial beauty analysis has emerged as a crucial and fascinating subject of human culture. With various applications and significant attention from researchers, recent studies have investigated the relationship between facial features and age, emotions, and other factors using multidisciplinary approaches. Facial beauty prediction is a significant visual recognition problem in the assessment of facial attractiveness, which is consistent with human perception. Overcoming the challenges associated with facial beauty prediction requires considerable effort due to the field’s novelty and lack of resources. In this vein, a deep learning method has recently demonstrated remarkable abilities in feature representation and analysis. Accordingly, this paper proposes an ensemble based on pre-trained convolutional neural network models to identify scores for facial beauty prediction. These ensembles are three separate deep convolutional neural networks, each with a unique structural representation built by previously trained models from Inceptionv3, Mobilenetv2, and a new simple network based on Convolutional Neural Networks (CNNs) for facial beauty prediction problems. According to the SCUT-FBP5500 benchmark dataset, the obtained 0.9350 Pearson coefficient experimental result demonstrated that using this ensemble of deep networks leads to a better prediction of facial beauty closer to human evaluation than conventional technology that spreads facial beauty. Finally, potential research directions are suggested for future research on facial beauty prediction.

• facial beauty prediction
• facial attractiveness
• ensemble learning
• pre-trained convolutional neural networks
• InceptionV3
• MobileNetV2

• Face recognition and analysis
• Advanced Neural Network Applications
• Evolutionary Psychology and Human Behavior
• facial beauty prediction
• facial attractiveness
• ensemble learning
• pre-trained convolutional neural networks
• InceptionV3
• MobileNetV2
• Computer Vision and Pattern Recognition
• Experimental and Cognitive Psychology
• Computer Science
• Psychology