Female Facial Beauty Analysis Using Transfer Learning and Stacking Ensemble Model

Abstract

Automatic analysis of facial beauty has become an emerging research topic in recent years and has fascinated many researchers. One of the key challenges of facial attractiveness prediction is to obtain accurate and discriminative face representation. This study provides a new framework to analyze the attractiveness of female faces using transfer learning methodology as well as stacking ensemble model. Specifically, a pre-trained Convolutional Neural Network (CNN) originally trained on relatively similar datasets for face recognition task, namely Ms-Celeb-1M and VGGFace2, is utilized to acquire high-level and robust features of female face images. This is followed by leveraging a stacking ensemble model which combines the predictions of several base models to predict the attractiveness of a face. Extensive experiments conducted on SCUT-FBP and SCUT-FBP 5500 benchmark datasets, confirm the strong robustness of the proposed approach. Interestingly, prediction correlations of 0.89 and 0.91 are achieved by our new method for SCUT-FBP and SCUT-FBP5500 datasets, respectively. This would indicate significant advantages over the other state-of-the-art work. Moreover, our successful results would certainly support the efficacy of transfer learning when applying deep learning techniques to compute facial attractiveness.