Artificial Child Face Database

Database of 500 artificially generated images of child faces.

Publication:
Thierry, S.M., Uddenberg, S., Albohn, D.N., & Todorov, A. (2025). Capturing variability in children’s faces: an artificial, yet realistic, face stimulus set. Frontiers in Psychology.

Abstract:
Children’s faces are underrepresented in face databases, and existing databases that do focus on children tend to have limitations in terms of the number of faces available and the diversity of ages and ethnicities represented. To improve the availability of children’s faces for experimental research purposes, we created a novel face database that contains 500 artificial images of children that are diverse in terms of both age (ages 3 to 10) and ethnicity (representing 15 different racial or ethnic groups). Using deep neural networks, we produced a large collection of synthetic photographs that look like naturalistic, realistic faces of children. To assess the representativeness of the dataset, adult participants (N = 585) judged the age, gender, ethnicity, and emotion of artificial faces selected from the set of 500 images. The images present a diverse array of artificial children’s faces, offering a valuable resource for research requiring children’s faces. The images and ratings are publicly available to researchers on Open Science Framework.

This database is made available under Creative Commons BY-NC-SA 4.0 license.

You can use, redistribute, and adapt it for non-commercial purposes, as long as you (a) give appropriate credit by citing our paper, (b) indicate any changes that you've made, and (c) distribute any derivative works under the same license.

https://creativecommons.org/licenses/by-nc-sa/4.0/

The face dataset used to create the underlying models that in turn were used to generate our synthetic faces can be found at:

https://github.com/NVlabs/ffhq-dataset