The Facial Beauty Prediction of the Model of Deep PCANet

Deep convolution neural network(DCNN) has achieved good results in face recognition, image classification and object detection, and has been widely used. However, DCNN has some problems in the facial beauty prediction, such as bad fitting effect, hard training etc. Deep PCANet model, using Principal Component Analysis Network (PCANet) as feature extractor. The model can get parameters through unsupervised pre-training with the advantages of less time in training and faster speed in feature extraction, which can effectively avoid the problems of DCNN. For this reason, the deep PCANet is introduced into the facial beauty prediction, and the training set image adopts multi-scale preprocessing to train the deep PCANet. The model can extract the structural global features of the face image, and the feature enhancement method can generate features with more representative capabilities. Finally, linear Support Vector Machine (SVM) and Random Forest (RF) regressors were used for training and prediction. Experimental results for facial beauty prediction on SCUT-FBP database show that deep PCANet has advantages of simpler structure, faster feature extraction, and few parameter adjustment; choosing the right image scale and using feature enhancement methods can improve the face aesthetic evaluation results, which proves that the network is valid and feasible.