  Two challenges lie in the facial attractiveness computation research: the
lack of true attractiveness labels (scores), and the lack of an accurate face
representation. In order to address the first challenge, this paper recasts
facial attractiveness computation as a label distribution learning (LDL)
problem rather than a traditional single-label supervised learning task. In
this way, the negative influence of the label incomplete problem can be
reduced. Inspired by the recent promising work in face recognition using deep
neural networks to learn effective features, the second challenge is expected
to be solved from a deep learning point of view. A very deep residual network
is utilized to enable automatic learning of hierarchical aesthetics
representation. Integrating these two ideas, an end-to-end deep learning
framework is established. Our approach achieves the best results on a standard
benchmark SCUT-FBP dataset compared with other state-of-the-art work.
