  This paper proposes a deep leaning method to address the challenging facial
attractiveness prediction problem. The method constructs a convolutional neural
network of facial beauty prediction using a new deep cascaded fine-turning
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 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 were involved in facial
beauty perception, which is consistent with the result of recent psychology
studies. Furthermore, we analyze the high-level 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 moth, are the most significant facial attributes for facial
attractiveness prediction, which is also consistent with the visual perception
intuition of human.
