A new humanlike facial attractiveness predictor with cascaded fine-tuning deep learning model

This paper proposes a deep leaning method to address the challenging facial attractiveness prediction problem. The method constructs a convolutional neural network (CNN) for facial beauty prediction using a new deep cascaded fine tuning scheme with various face inputting channels, such as the original RGB face image, the detail layer image, and the lighting layer image. With a carefully designed CNN model of deep structure, large input size and small convolutional kernels, we have achieved a high prediction correlation of 0.88. This result convinces us that the problem of the facial attractiveness prediction can be solved by deep learning approach, and it also shows the important roles of the facial smoothness, lightness, and color information that involve in facial beauty evaluation, which is consistent with the result of recent psychology studies. Furthermore, we analyze the highlevel features learnt by CNN through visualization of its hidden layers, and some interesting phenomena were observed. It is found that the contours and appearance of facial features (especially eyes and month) are the most significant facial attributes for facial attractiveness prediction, which is also consistent with visual perception intuition of human.