ArticleScientific data2023
A Chinese Face Dataset with Dynamic Expressions and Diverse Ages Synthesized by Deep Learning.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Creating and validating photorealistic AI-generated facial expression stimuli for emotion research.Behavior research methods · 2026Article
- A Topology Standardized 3D Facial Dataset with Emotion and Action Unit Diversity for East Asians.Scientific data · 2026Article
- Arousal increases locus coeruleus blood flow, salience-related brain responses, and modulates negative-valence attentional biases.bioRxiv : the preprint server for biology · 2025Article
- A generated image repository of aging faces.Scientific data · 2025Article
- AcuSim: A Synthetic Dataset for Cervicocranial Acupuncture Points Localisation.Scientific data · 2025Article
- A Chinese Face Dataset with Dynamic Expressions and Diverse Ages Synthesized by Deep Learning.Scientific data · 2023Article
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Authors and funding
6 authors.
Funding
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.
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Registered trials
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