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

Label distribution based facial attractiveness computation by deep residual learning