# Label distribution based facial attractiveness computation by deep residual learning Source: [Label distribution based facial attractiveness computation by deep residual learning]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:51:52.529Z Authors: Shu Liu, Bo Li, Yangyu Fan, Zhe Guo, Ashok Samal Published: 2016-09-02T08:08:39.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Facial attractiveness estimation on SCUT-FBP 20% (test)](): Facial attractiveness estimation; SCUT-FBP 20% (test); MAE: 0.213. ## Original sources and implementation [Original paper]() [PDF]()