捕捉儿童脸部的变化:一个人造的,但现实的,面部刺激集
Sophia M Thierry1, Stefan Uddenberg2, Daniel Albohn3
1Department of Psychology, Brock University, St. Catharines, ON, Canada.
Frontiers in psychology
|September 18, 2025
概括
一个新的数据集由500个人工儿童面孔组成,年龄和种族各异. 本资源解决了当前研究数据库中儿童脸部代表性不足的问题.
科学领域:
- 计算机视觉 计算机视觉
- 发展心理学 发展心理学
- 人与计算机的交互
背景情况:
- 现有的儿童面部数据集在规模和多样性上是有限的.
- 儿童脸部的代表性不足阻碍了发展心理学和人工智能的研究.
- 需要对儿童脸部进行全面和多样化的数据集.
研究的目的:
- 创建一个新的,大规模的,和各种数据集的人工儿童的脸.
- 为涉及儿童脸部的实验研究提供一个有价值的资源.
- 在面部图像数据库中增强年龄和种族的多样性.
主要方法:
- 利用深度神经网络生成500个合成,现实的儿童面部图像.
- 确保跨年龄 (3-10岁) 和15个不同的种族群体的多样性.
- 收集了585名成年参与者的评分,以验证年龄,性别,种族和情感感知.
主要成果:
- 生成的数据集包括500个多样化的人造儿童面孔.
- 成年参与者成功地确定了合成面部的关键属性 (年龄,性别,种族,情感).
- 数据集在特定的人口变量中显示了代表性.
结论:
- 这一新型数据集为需要儿童面部图像的研究提供了重大进展.
- 在开放科学框架上公开的可用性促进了更广泛的科学访问和利用.
- 该资源可以支持面部识别,发育研究和AI偏见缓解等领域的研究.
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