2.5d facial attractiveness computation based on data-driven geometric ratios

Shu Liu, Yangyu Fan, Zhe Guo, Ashok Samal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Computational approaches to investigating face attractiveness have become an emerging topic in facial analysis research. Integrating techniques from image analysis, pattern recognition and machine learning, this subarea aims to explore the nature, components and impacts of facial attractiveness and to develop computational algorithms to analyze the attractiveness of a face. In this paper we develop an attractiveness computation model for both frontal and profile images (2.5D). We focus on the role of geometric ratios in the determination of facial attractivenss. Stepwise regression is used as the feature selection method to select the discriminatory variables from a huge set of data-driven ratios. Decision tree is then used to generate an automated classifier for both frontal and profile computation models. The BJUT-3D Face Database is pre-processed and tested as our experimental dataset. The low statistic errors and high correlation indicate the accuracy of our computation models.

Original languageEnglish (US)
Title of host publicationIntelligence Science and Big Data Engineering
Subtitle of host publicationImage and Video Data Engineering - 5th International Conference, IScIDE 2015, Revised Selected Papers
EditorsXiaofei He, Zhi-Hua Zhou, Xinbo Gao, Zhi-Yong Liu, Yanning Zhang, Baochuan Fu, Fuyuan Hu, Zhancheng Zhang
PublisherSpringer Verlag
Pages564-573
Number of pages10
ISBN (Print)9783319239873
DOIs
StatePublished - 2015
Event5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015 - Suzhou, China
Duration: Jun 14 2015Jun 16 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9242
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015
CountryChina
CitySuzhou
Period6/14/156/16/15

Keywords

  • 2.5D
  • BJUT-3D
  • Data-driven
  • Face ratios
  • Facial attractiveness computation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Liu, S., Fan, Y., Guo, Z., & Samal, A. (2015). 2.5d facial attractiveness computation based on data-driven geometric ratios. In X. He, Z-H. Zhou, X. Gao, Z-Y. Liu, Y. Zhang, B. Fu, F. Hu, & Z. Zhang (Eds.), Intelligence Science and Big Data Engineering: Image and Video Data Engineering - 5th International Conference, IScIDE 2015, Revised Selected Papers (pp. 564-573). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9242). Springer Verlag. https://doi.org/10.1007/978-3-319-23989-7_57