A Chinese Face Dataset with Dynamic Expressions and Diverse Ages Synthesized by Deep Learning

Shangfeng Han, Yanliang Guo, Xinyi Zhou, Junlong Huang, Linlin Shen, Yuejia Luo

Research output: Journal PublicationArticlepeer-review

1 Citation (Scopus)

Abstract

Facial stimuli have gained increasing popularity in research. However, the existing Chinese facial datasets primarily consist of static facial expressions and lack variations in terms of facial aging. Additionally, these datasets are limited to stimuli from a small number of individuals, in that it is difficult and time-consuming to recruit a diverse range of volunteers across different age groups to capture their facial expressions. In this paper, a deep-learning based face editing approach, StyleGAN, is used to synthesize a Chinese face dataset, namely SZU-EmoDage, where faces with different expressions and ages are synthesized. Leverage on the interpolations of latent vectors, continuously dynamic expressions with different intensities, are also available. Participants assessed emotional categories and dimensions (valence, arousal and dominance) of the synthesized faces. The results show that the face database has good reliability and validity, and can be used in relevant psychological experiments. The availability of SZU-EmoDage opens up avenues for further research in psychology and related fields, allowing for a deeper understanding of facial perception.

Original languageEnglish
Article number878
JournalScientific data
Volume10
Issue number1
DOIs
Publication statusPublished - Dec 2023
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

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