Chinese Face and Body Dataset (CFBD): Lab and personal photos of the same individuals.

Face stimuli used in face perception research often focus on between-model variability, underrepresenting within-model variability across conditions and time. However, exposure to within-model variability is crucial for developing stable representations of faces. Here, we introduce the Chinese Face and Body Dataset (CFBD), a publicly accessible resource that captures within-model variability to represent a broad spectrum of appearance and image variations in laboratory and natural settings. The CFBD comprises 2,195 images from 117 models, including both laboratory photos taken by researchers and personal photos donated by models. Each model is depicted in 10 to 31 photos, coded for attributes such as the time photos were taken, facial expressions, viewing angles, and environmental contexts. Independent participants also rated these photos based on facial attractiveness, trustworthiness, and distinctiveness. The results revealed that the CFBD captures a wide range of variations across appearances and image attributes, and the within-model variances in trait ratings are comparable to, if not greater than, the between-model variances. Moreover, within-model variances in the trait ratings differ by image type, with personal photos being rated as more attractive, distinctive, and trustworthy than their laboratory counterparts. By capturing a diverse range of appearances and images of Chinese individuals, the CFBD provides valuable resources that expand face datasets, potentially advancing our understanding of robust face representation.