A landmark-based data-driven approach on 2.5D facial attractiveness computation

Shu Liu, Yang Yu Fan, Zhe Guo, Ashok Samal, Afan Ali

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Investigating the nature and components of face attractiveness from a computational view has become an emerging topic in facial analysis research. In this paper, a multi-view (frontal and profile view, 2.5D) facial attractiveness computational model is developed to explore how face geometry affects its attractiveness. A landmark-based, data-driven method is introduced to construct a huge dimension of three kinds of geometric facial measurements, including ratios, angles, and inclinations. An incremental feature selection algorithm is proposed to systematically select the most discriminative subset of geometric features, which are finally mapped to an attractiveness score through the application of support vector regression (SVR). On a dataset of 360 facial images pre-processed from BJUT-3D Face Database and an attractiveness score dataset collected from human raters, we show that the computational model performs well with low statistic error (MSE=0.4969) and good predictability (R2=0.5756).

Original languageEnglish (US)
Pages (from-to)168-178
Number of pages11
StatePublished - May 17 2017


  • 2.5 D
  • BJUT-3D
  • Data-driven
  • Facial attractiveness computation
  • Geometric features

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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