Human-like evaluation by facial attractiveness intelligent machine

Abstract

Facial attractiveness is an important factor in social interactions and has been widely studied in psychology and neuroscience. This paper presents a novel approach to the problem of predicting facial attractiveness using machine learning and computer vision techniques. Our main objective is to investigate whether an intelligent machine can learn and accurately predict facial attractiveness based on objective rules in facial features.

To achieve this, we collected datasets of facial images and corresponding attractiveness rankings for women. We then utilized various machine learning methods, including k-nearest neighbors (KNN) and support vector regression (SVR), to train a predictor model that learned from these datasets to provide a human-like assessment of facial attractiveness. The model used facial feature parameters, such as symmetry and proportion, as input to determine the attractiveness ranking as output.

We evaluated the performance of our trained predictor model using several metrics, including the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute percentage error (MAPE). The best performance was achieved using the KNN algorithm during the testing phase, with R2=0.9902, RMSE=0.0056, and MAPE=0.0856. It indicated a significant improvement in the accuracy of facial attractiveness prediction compared to previous studies.

Our results demonstrate that an intelligent machine can learn and predict facial attractiveness based on objective rules in facial features, providing a promising approach for ranking facial attractiveness. In comparison to previous studies in this area, our approach shows significant improvement in accuracy, with a correlation coefficient higher than that of human ratings. This work has significant implications for the fields of psychology, neuroscience, and computer science, as it provides a new perspective on the concept of facial attractiveness and its quantification using machine learning.