Abstract

The analysis of facial beauty holds considerable importance in social interactions and has emerged as a prominent research topic in various fields. Automatic assessment of facial attractiveness has garnered significant interest due to its wide range of potential applications. This research aims to pinpoint the primary attributes contributing to beauty and assess the influence of various facial angles, such as those of the eyes, nose, lips, chin, eyebrows, and jaws. Additionally, it examines the significance of geometric facial measurements, encompassing distances between facial landmarks and ratios, in the context of beauty evaluation. The study also employs two techniques, namely Principal Component Analysis (PCA) and stacked regression, to predict the attractiveness of faces. The experimental data set used for evaluation is the well-known SCUT-FBP benchmark database (consisting of 500 facial images). The obtained results demonstrate the superiority of our method, which achieved the highest Pearson's Correlation Coefficient (PCC) of 0.8043 and the lowest Mean Absolute Errors (MAE) of 0.3068 among all the evaluated methods. Our method incorporates six angles and leverages PCA for dimensionality reduction, along with 23 distance features, resulting in improved accuracy. Furthermore, the utilization of the stacking ensemble approach instead of individual machine learning methods contributes to the impressive performance of our method. This unequivocal success underscores the pivotal role of machine learning and pattern recognition in providing an insightful and precise assessment of facial beauty.