Automated classification of female facial beauty by image analysis and supervised learning

Hatice Gunes, Massimo Piccardi, Tony Jan

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)


The fact that perception of facial beauty may be a universal concept has long been debated amongst psychologists and anthropologists. In this paper, we performed experiments to evaluate the extent of beauty universality by asking a number of diverse human referees to grade a same collection of female facial images. Results obtained show that the different individuals gave similar votes, thus well supporting the concept of beauty universality. We then trained an automated classifier using the human votes as the ground truth and used it to classify an independent test set of facial images. The high accuracy achieved proves that this classifier can be used as a general, automated tool for objective classification of female facial beauty. Potential applications exist in the entertainment industry and plastic surgery.

Original languageEnglish
Pages (from-to)968-978
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Issue numberPART 2
Publication statusPublished - 2004
Externally publishedYes
EventVisual Communications and Image Processing 2004 - San Jose, CA, United States
Duration: 20 Jan 200422 Jan 2004


  • Facial beauty classification
  • Facial features
  • Golden proportion
  • Performance evaluation
  • Supervised learning


Dive into the research topics of 'Automated classification of female facial beauty by image analysis and supervised learning'. Together they form a unique fingerprint.

Cite this