Label Distribution-Based Facial Attractiveness Computation by Deep Residual Learning

Yang Yu Fan, Shu Liu, Bo Li, Zhe Guo, Ashok Samal, Jun Wan, Stan Z. Li

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


Two key challenges lie in the facial attractiveness computation research: the lack of discriminative face representations, and the scarcity of sufficient and complete training data. Motivated by recent promising work in face recognition using deep neural networks to learn effective features, the first challenge is expected to be addressed from a deep learning point of view. A very deep residual network is utilized to enable automatic learning of hierarchical aesthetics representation. The inspiration to deal with the second challenge comes from the natural representation of the training data, where each training face can be associated with a label (score) distribution given by human raters rather than a single label (average score). This paper, therefore, recasts facial attractiveness computation as a label distribution learning problem. Integrating these two ideas, an end-to-end attractiveness learning framework is established. We also perform feature-level fusion by incorporating the low-level geometric features to further improve the computational performance. Extensive experiments are conducted on a standard benchmark, the SCUT-FBP dataset, where our approach shows significant advantages over the other state-of-the-art work.

Original languageEnglish (US)
Pages (from-to)2196-2208
Number of pages13
JournalIEEE Transactions on Multimedia
Issue number8
StatePublished - Aug 2018


  • Facial attractiveness computation
  • deep residual network
  • feature fusion
  • label distribution

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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