Facial beauty prediction via deep cascaded forest

Abstract

Facial beauty prediction (FBP), which is a prediction based on the classification of human facial beauty, has been applied in some social platforms and entertainment software. However, among the various approaches to FBP, methods based convolutional network is too complicated, and traditional methods cannot achieve the desired performance. In this paper, we propose a method for FBP via deep cascade forest. This method uses multi-grained scanning to obtain the features of the image, and uses multiple random forests to enhance the features. Then multiple classifiers to form a new classifier, which is used for predicting the acquired features to complete the FBP task. This method shows the advantages of feature extraction and relatively high prediction accuracy in 10,000 facial beauty datasets (10TFBD). And we are optimised for the cascade forest part and further improved the prediction accuracy. Our experiments demonstrate the effectiveness of FBP tasks.