Automatic Classification of Chinese Female Facial Beauty using Support Vector Machine

Beauty is a universal concept which has long been explored by philosophers, artists and psychologists, but there are few implementations of automated facial beauty assessment in computational science. In this paper, we develop an automated Chinese female facial beauty classification system through the application of machine learning algorithm of SVM (Support Vector Machine). We present a simple but effective feature extraction for facial beauty classification. 17 geometric features are designed to abstractly represent each facial image. The experiment is based on 510 facial images, high accuracy of 95.3% is obtained for 2-level classification (beautiful or not), but the accuracy of 4-level classification is 77.9% by SVM. The results clearly show that the notion of beauty perceived by human can also be learned by machine through the employment of supervised learning techniques. Furthermore, the finding of big gap between the accuracy of 2-level classification and 4-level classification is interesting and surprising: the high accuracy naturally leads to the conclusion that there indeed exists simplicity and objectiveness underlying the judgment of aesthetical ideal facial attractiveness; In contrast, the relatively low accuracy for 4-level classification indicates that the presented simple feature vectors is not sufficient for the classification of other levels of facial attractiveness.