IDEAS home Printed from https://ideas.repec.org/a/ids/ijdsci/v10y2025i2p119-135.html
   My bibliography  Save this article

Beauty aids: can AI improve human behaviours with imperfect data?

Author

Listed:
  • Wen-Feng Wang
  • Bai-Zhou Xu
  • Bin Hu
  • Fuqing Li
  • Lalit Mohan Patnaik
  • Lu-Jie Cui
  • Yun-Zhu Pan

Abstract

This paper aims to examine whether AI can improve human behaviours with imperfect data. Beauty aids with the pretrained AI model is taken as a practical example. This model integrated fuzzy reasoning with ResNet-50 for facial beauty prediction (FBP) and real-time recommendations of makeup behaviours. Results shown that the AI model can provide beauty aids for people whose facial data have not be included during the pretraining process and improve their makeup behaviours. The difference between the maximal and minimal values amounts to 33.62, implying that the effect of beauty aids is evident. The cross validation with perfect data further also confirmed that the effects of increased makeup experiences are worthy of further attention. The recommended degree of powder makeup for the volunteer is 0.118~0.2, while that of lipstick and blush makeup is 0.034~0.2. As an emerging technique, potential evolutions of the real-time beauty aids system with AI and data science will bring out the long-term future of FBP research.

Suggested Citation

  • Wen-Feng Wang & Bai-Zhou Xu & Bin Hu & Fuqing Li & Lalit Mohan Patnaik & Lu-Jie Cui & Yun-Zhu Pan, 2025. "Beauty aids: can AI improve human behaviours with imperfect data?," International Journal of Data Science, Inderscience Enterprises Ltd, vol. 10(2), pages 119-135.
    • Handle: RePEc:ids:ijdsci:v:10:y:2025:i:2:p:119-135
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=149851
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijdsci:v:10:y:2025:i:2:p:119-135. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=429 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.