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

Shu Liu; Yangyu Fan; Zhe Guo; Ashok Samal

doi:10.1007/978-3-319-23989-7_57

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 proceeding › Conference contribution

9 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 language	English (US)
Title of host publication	Intelligence Science and Big Data Engineering
Subtitle of host publication	Image and Video Data Engineering - 5th International Conference, IScIDE 2015, Revised Selected Papers
Editors	Xiaofei He, Zhi-Hua Zhou, Xinbo Gao, Zhi-Yong Liu, Yanning Zhang, Baochuan Fu, Fuyuan Hu, Zhancheng Zhang
Publisher	Springer Verlag
Pages	564-573
Number of pages	10
ISBN (Print)	9783319239873
DOIs	https://doi.org/10.1007/978-3-319-23989-7_57
State	Published - 2015
Event	5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015 - Suzhou, China Duration: Jun 14 2015 → Jun 16 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9242
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015
Country/Territory	China
City	Suzhou
Period	6/14/15 → 6/16/15

Keywords

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

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-23989-7_57

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

2.5d facial attractiveness computation based on data-driven geometric ratios. / Liu, Shu; Fan, Yangyu; Guo, Zhe et al.
Intelligence Science and Big Data Engineering: Image and Video Data Engineering - 5th International Conference, IScIDE 2015, Revised Selected Papers. ed. / Xiaofei He; Zhi-Hua Zhou; Xinbo Gao; Zhi-Yong Liu; Yanning Zhang; Baochuan Fu; Fuyuan Hu; Zhancheng Zhang. Springer Verlag, 2015. p. 564-573 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9242).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9242, Springer Verlag, pp. 564-573, 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015, Suzhou, China, 6/14/15. https://doi.org/10.1007/978-3-319-23989-7_57

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

Liu, Shu ; Fan, Yangyu ; Guo, Zhe et al. / 2.5d facial attractiveness computation based on data-driven geometric ratios. Intelligence Science and Big Data Engineering: Image and Video Data Engineering - 5th International Conference, IScIDE 2015, Revised Selected Papers. editor / Xiaofei He ; Zhi-Hua Zhou ; Xinbo Gao ; Zhi-Yong Liu ; Yanning Zhang ; Baochuan Fu ; Fuyuan Hu ; Zhancheng Zhang. Springer Verlag, 2015. pp. 564-573 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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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author = "Shu Liu and Yangyu Fan and Zhe Guo and Ashok Samal",

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doi = "10.1007/978-3-319-23989-7_57",

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PY - 2015

Y1 - 2015

N2 - 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.

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

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