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“Wow! You Are So Beautiful Today!”

Published: 01 October 2014 Publication History

Abstract

Beauty e-Experts, a fully automatic system for makeover recommendation and synthesis, is developed in this work. The makeover recommendation and synthesis system simultaneously considers many kinds of makeover items on hairstyle and makeup. Given a user-provided frontal face image with short/bound hair and no/light makeup, the Beauty e-Experts system not only recommends the most suitable hairdo and makeup, but also synthesizes the virtual hairdo and makeup effects. To acquire enough knowledge for beauty modeling, we built the Beauty e-Experts Database, which contains 1,505 female photos with a variety of attributes annotated with different discrete values. We organize these attributes into two different categories, beauty attributes and beauty-related attributes. Beauty attributes refer to those values that are changeable during the makeover process and thus need to be recommended by the system. Beauty-related attributes are those values that cannot be changed during the makeup process but can help the system to perform recommendation. Based on this Beauty e-Experts Dataset, two problems are addressed for the Beauty e-Experts system: what to recommend and how to wear it, which describes a similar process of selecting hairstyle and cosmetics in daily life. For the what-to-recommend problem, we propose a multiple tree-structured supergraph model to explore the complex relationships among high-level beauty attributes, mid-level beauty-related attributes, and low-level image features. Based on this model, the most compatible beauty attributes for a given facial image can be efficiently inferred. For the how-to-wear-it problem, an effective and efficient facial image synthesis module is designed to seamlessly synthesize the recommended makeovers into the user facial image. We have conducted extensive experiments on testing images of various conditions to evaluate and analyze the proposed system. The experimental results well demonstrate the effectiveness and efficiency of the proposed system.

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Cited By

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  • (2024)A Study on the Impact of Quality and HCI Factors of a Deep Learning-Based Makeup Recommendation System on Acceptance Intention: Focusing on the Mediating Effects of Users’ Perceived Trust and ImmersionJournal of the Korean Society of Cosmetology10.52660/JKSC.2024.30.5.96030:5(960-970)Online publication date: 31-Oct-2024
  • (2024)Dynamic Attentive Convolution for Facial Beauty PredictionIEICE Transactions on Information and Systems10.1587/transinf.2023EDL8058E107.D:2(239-243)Online publication date: 1-Feb-2024
  • (2024)Color Transfer for Images: A SurveyACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363515220:8(1-29)Online publication date: 9-Jul-2024
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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications
ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 11, Issue 1s
Special Issue on Multiple Sensorial (MulSeMedia) Multimodal Media : Advances and Applications
September 2014
260 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/2675060
Issue’s Table of Contents
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 October 2014
Accepted: 01 June 2014
Revised: 01 June 2014
Received: 01 February 2014
Published in TOMM Volume 11, Issue 1s

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Author Tags

  1. Beauty recommendation
  2. beauty synthesis
  3. multiple tree-structured super-graphs model

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Cited By

View all
  • (2024)A Study on the Impact of Quality and HCI Factors of a Deep Learning-Based Makeup Recommendation System on Acceptance Intention: Focusing on the Mediating Effects of Users’ Perceived Trust and ImmersionJournal of the Korean Society of Cosmetology10.52660/JKSC.2024.30.5.96030:5(960-970)Online publication date: 31-Oct-2024
  • (2024)Dynamic Attentive Convolution for Facial Beauty PredictionIEICE Transactions on Information and Systems10.1587/transinf.2023EDL8058E107.D:2(239-243)Online publication date: 1-Feb-2024
  • (2024)Color Transfer for Images: A SurveyACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363515220:8(1-29)Online publication date: 9-Jul-2024
  • (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)Facial Aesthetic Enhancement Network for Asian Faces Based on Differential Facial Aesthetic ActivationsICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447427(3785-3789)Online publication date: 14-Apr-2024
  • (2024)Effects of virtual makeups' perceived augmentation on consumers' perceived valueAsia Pacific Journal of Marketing and Logistics10.1108/APJML-02-2024-023737:2(365-381)Online publication date: 26-Jul-2024
  • (2024)Learning feature alignment across attribute domains for improving facial beauty predictionExpert Systems with Applications10.1016/j.eswa.2024.123644249(123644)Online publication date: Sep-2024
  • (2024)Personalizing human avatars based on realistic 3D facial reconstructionMultimedia Tools and Applications10.1007/s11042-024-19583-083:39(86593-86620)Online publication date: 22-Jun-2024
  • (2024)SRPSGAN: Super-resolution with pose and expression robust spatial-aware generative adversarial network for makeup transferMultimedia Tools and Applications10.1007/s11042-023-15440-883:4(10147-10165)Online publication date: 1-Jan-2024
  • (2024)A multi-granularity facial extreme makeup transfer and removal model with local-global collaborationApplied Intelligence10.1007/s10489-024-05692-854:20(9741-9759)Online publication date: 1-Oct-2024
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