Investigation of Facial Preference Using Gaussian Process Preference Learning and Generative Image Model

Masashi Komori*, Keito Shiroshita, Masataka Nakagami, Koyo Nakamura, Maiko Kobayashi, Katsumi Watanabe

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study introduces a novel approach to investigate human facial attractiveness’s intrinsic psychophysical function using a sequential experimental design with a combination of Bayesian optimization (BO) and StyleGAN2. To estimate a facial attractiveness function from pairwise comparison data, we used a BO that incorporates Gaussian process preference learning (GPPL). Fifty female Japanese university students provided facial photographs. We embedded each female facial image into a latent representation (18 × 512 dimensions) in the StyleGAN2 network trained on the Flickr-Faces-HQ (FFHQ) dataset. Using PCA, the latent representations’ dimension is reduced to an 8-dimensional subspace, which we refer to here as the Japanese female face space. Nine participants participated in the pairwise comparison task. They had to choose the more attractive facial images synthesized using StyleGAN2 in the face subspace and provided their evaluations in 100 trials. The stimuli for the first 80 trials were created from randomly generated parameters in the face subspace, while the remaining 20 trials were created from the parameters calculated using the acquisition function. We estimated the facial parameters corresponding to the most, the least, 25, 50, 75 percentile rank of attractiveness and reconstructed the faces based on the results. The results show that a combination of StyleGAN2 and GPPL methodologies is an effective way to elucidate human kansei evaluations of complex stimuli such as human faces.

Original languageEnglish
Title of host publicationComputer Information Systems and Industrial Management - 20th International Conference, CISIM 2021, Proceedings
EditorsKhalid Saeed, Jiří Dvorský
PublisherSpringer Science and Business Media Deutschland GmbH
Pages193-202
Number of pages10
ISBN (Print)9783030843397
DOIs
Publication statusPublished - 2021
Event20th International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2021 - Ełk, Poland
Duration: 2021 Sep 242021 Sep 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12883 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2021
Country/TerritoryPoland
CityEłk
Period21/9/2421/9/26

Keywords

  • Bayesian optimization
  • Gaussian process preference learning
  • ICBAKE
  • StyleGAN2

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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