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Advances in computational facial attractiveness methods

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Abstract

Attractiveness of a face plays an important role in many social endeavors. It influences careers like digital entertainment, modeling and acting, as well as person’s career prospect, financial status, and personal relationships. Computational approaches to exploring the nature and components of face attractiveness have been proposed, and have become an emerging topic in facial analysis research. Integrating techniques from image processing, computer vision and machine learning, this subarea aims to develop computational methods to quantify and investigate the attractiveness of a face. This paper summarizes the most recent advances in four related aspects of face attractiveness: (a) facial attractiveness prediction, (b) facial attractiveness enhancement, (c) lateral facial attractiveness and (d) 3D facial attractiveness. The motivations, innovative techniques, and significant results are summarized and discussed. The open problems in these areas and directions for future work are also briefly stated.

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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 2015JM6317, the Fundamental Research Funds for the Central Universities under Grant 3102014JCQ01060.

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Authors and Affiliations

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

    Shu Liu, Yang-Yu Fan & Zhe Guo

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

    Shu Liu & Ashok Samal

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  1. Shu Liu
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Correspondence to Shu Liu.

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Liu, S., Fan, YY., Samal, A. et al. Advances in computational facial attractiveness methods. Multimed Tools Appl 75, 16633–16663 (2016). https://doi.org/10.1007/s11042-016-3830-3

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