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 in 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 for 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 contains main contributions propose an ensemble based on the pre-trained convolutional neural networks 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 problem. According to the SCUT-FBP5500 benchmark dataset the model obtains 0.9350 Pearson Coefficient Experimental results demonstrated that using this ensemble of deep network leads to better predicting of facial beauty closer to human evaluation than conventional technology that spreads the facial beauty. Finally, potential research directions are suggested for future research on facial beauty prediction.