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

School of Electronics and Information

Northwestern Polytechnical University Xian
University of Nebraska-Lincoln

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

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
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: 14 Jun 2015 → 16 Jun 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

Conference

Conference	5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015
Country/Territory	China
City	Suzhou
Period	14/06/15 → 16/06/15

Keywords

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

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

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)).

@inproceedings{7092edc41155435a8e29527923e92472,

title = "2.5d facial attractiveness computation based on data-driven geometric ratios",

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.",

keywords = "2.5D, BJUT-3D, Data-driven, Face ratios, Facial attractiveness computation",

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

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015 ; Conference date: 14-06-2015 Through 16-06-2015",

year = "2015",

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

language = "英语",

isbn = "9783319239873",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "564--573",

editor = "Xiaofei He and Zhi-Hua Zhou and Xinbo Gao and Zhi-Yong Liu and Yanning Zhang and Baochuan Fu and Fuyuan Hu and Zhancheng Zhang",

booktitle = "Intelligence Science and Big Data Engineering",

}

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, 14/06/15. 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 › peer-review

TY - GEN

T1 - 2.5d facial attractiveness computation based on data-driven geometric ratios

AU - Liu, Shu

AU - Fan, Yangyu

AU - Guo, Zhe

AU - Samal, Ashok

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

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.

KW - 2.5D

KW - BJUT-3D

KW - Data-driven

KW - Face ratios

KW - Facial attractiveness computation

UR - https://www.scopus.com/pages/publications/84951770817

U2 - 10.1007/978-3-319-23989-7_57

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

M3 - 会议稿件

AN - SCOPUS:84951770817

SN - 9783319239873

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 564

EP - 573

BT - Intelligence Science and Big Data Engineering

A2 - He, Xiaofei

A2 - Zhou, Zhi-Hua

A2 - Gao, Xinbo

A2 - Liu, Zhi-Yong

A2 - Zhang, Yanning

A2 - Fu, Baochuan

A2 - Hu, Fuyuan

A2 - Zhang, Zhancheng

PB - Springer Verlag

T2 - 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015

Y2 - 14 June 2015 through 16 June 2015

ER -

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

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

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this