@inproceedings{8521a4d004b74a919a99da6333c2d06a,
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 = "Funding Information: 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. 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 = "English (US)",
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",
}