Deep facial features for personalized attractiveness prediction

Irina Lebedeva; Yi Guo; Fangli Ying

doi:10.1117/12.2599699

30 June 2021 Deep facial features for personalized attractiveness prediction

Irina Lebedeva, Yi Guo, Fangli Ying

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 118780A (2021) https://doi.org/10.1117/12.2599699
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

In this work, we propose a novel personalized facial attractiveness prediction method that is able to effectively learn an individual’s preferences on few training images. A deep convolutional neural network (CNN) was first employed to estimate facial attributes. Then the attributes that play the most significant role for the individual were selected to train Random Forest. A new dataset specially created for personalized beauty evaluation was also proposed. Our method has achieved promising results of 54% Pearson’s Correlation on 15 training images, 61% on 35 images.

Citation Download Citation

Irina Lebedeva, Yi Guo, and Fangli Ying "Deep facial features for personalized attractiveness prediction", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 118780A (30 June 2021); https://doi.org/10.1117/12.2599699

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available