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Data-driven enhancement of facial attractiveness

Published: 01 August 2008 Publication History

Abstract

When human raters are presented with a collection of shapes and asked to rank them according to their aesthetic appeal, the results often indicate that there is a statistical consensus among the raters. Yet it might be difficult to define a succinct set of rules that capture the aesthetic preferences of the raters. In this work, we explore a data-driven approach to aesthetic enhancement of such shapes. Specifically, we focus on the challenging problem of enhancing the aesthetic appeal (or the attractiveness) of human faces in frontal photographs (portraits), while maintaining close similarity with the original.
The key component in our approach is an automatic facial attractiveness engine trained on datasets of faces with accompanying facial attractiveness ratings collected from groups of human raters. Given a new face, we extract a set of distances between a variety of facial feature locations, which define a point in a high-dimensional "face space". We then search the face space for a nearby point with a higher predicted attractiveness rating. Once such a point is found, the corresponding facial distances are embedded in the plane and serve as a target to define a 2D warp field which maps the original facial features to their adjusted locations. The effectiveness of our technique was experimentally validated by independent rating experiments, which indicate that it is indeed capable of increasing the facial attractiveness of most portraits that we have experimented with.

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cover image ACM Conferences
SIGGRAPH '08: ACM SIGGRAPH 2008 papers
August 2008
887 pages
ISBN:9781450301121
DOI:10.1145/1399504
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 August 2008

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Author Tags

  1. facial attractiveness
  2. machine learning
  3. optimization
  4. warping

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SIGGRAPH '08 Paper Acceptance Rate 90 of 518 submissions, 17%;
Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

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  • (2024)Broad Siamese Network for Facial Beauty PredictionIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.34292935:11(5786-5800)Online publication date: Nov-2024
  • (2024)VRetouchEr: Learning Cross-Frame Feature Interdependence with Imperfection Flow for Face Retouching in Videos2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00873(9141-9150)Online publication date: 16-Jun-2024
  • (2024)Anchor-Net: Distance-Based Self-Supervised Learning Model for Facial Beauty PredictionIEEE Access10.1109/ACCESS.2024.339487012(61375-61387)Online publication date: 2024
  • (2023)Blemish-aware and Progressive Face Retouching with Limited Paired Data2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.00542(5599-5608)Online publication date: Jun-2023
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  • (2021)Attribute-induced Attractiveness Regression of Facial Images with Multi-task Convolution Neural Network2021 16th International Conference on Computer Science & Education (ICCSE)10.1109/ICCSE51940.2021.9569262(816-821)Online publication date: 17-Aug-2021
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