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International Conference on Intelligent Science and Big Data Engineering

IScIDE 2015: Intelligence Science and Big Data Engineering. Image and Video Data Engineering pp 564–573Cite as

2.5D Facial Attractiveness Computation Based on Data-Driven Geometric Ratios

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9242))

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.

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Acknowledgments

This work is supported in part by China Scholarship Council (CSC) under Grant No. 201306290099, the National Natural Science Foundation of China under Grant 61402371, Science and Technology Innovation Engineering Plan in Shaanxi Province of China under Grant 2013SZS15-K02, Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2013JQ8039, the Fundamental Research Funds for the Central Universities under Grant 3102014JCQ01060, Graduate Starting Seed Fund of Northwestern Polytechnical University under Grant Z2013064. Portions of the research in this paper use the BJUT-3D Face Database collected under the joint sponsor of National Natural Science Foundation of China, Beijing Natural Science Foundation Program, Beijing Science and Educational Committee Program.

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Authors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shannxi, China

    Shu Liu, Yangyu Fan & Zhe Guo

  2. Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA

    Shu Liu & Ashok Samal

Authors
  1. Shu Liu
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  2. Yangyu Fan
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  3. Zhe Guo
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  4. Ashok Samal
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Corresponding author

Correspondence to Shu Liu .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xiaofei He

  2. Xidian University, Xi'an, China

    Xinbo Gao

  3. Northwestern Polytechnical University, Shaanxi, China

    Yanning Zhang

  4. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  5. Chinese Academy of Sciences, Beijing, China

    Zhi-Yong Liu

  6. Suzhou University of Science and Technology, Suzhou, China

    Baochuan Fu

  7. Suzhou University of Science and Technology, Jiangsu, China

    Fuyuan Hu

  8. Suzhou University of Science and Technology, Jiangsu, China

    Zhancheng Zhang

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© 2015 Springer International Publishing Switzerland

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Liu, S., Fan, Y., Guo, Z., Samal, A. (2015). 2.5D Facial Attractiveness Computation Based on Data-Driven Geometric Ratios. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_57

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  • DOI: https://doi.org/10.1007/978-3-319-23989-7_57

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23987-3

  • Online ISBN: 978-3-319-23989-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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