GANDiffKids: Benchmarking Face Recognition for Children on Synthetic Data

Face recognition systems are primarily tailored to adult faces, and numerous studies have shown that their performance significantly declines when applied to children. The development of child-specific recognition models is further constrained by the scarcity of publicly available datasets and the ethical and privacy concerns surrounding the collection of children's biometric data. To overcome these limitations, we propose GANDiffKids, a novel framework for synthesizing realistic child face images with diverse intra-class variations. Our approach combines a generative adversarial network (GAN) with a diffusion model - a strategy that has proven effective for adult face synthesis in prior work. We demonstrate that GANDiffKids can generate a large-scale dataset of synthetic child faces across a range of image qualities. The resulting dataset is benchmarked using a state-of-the-art face recognition model, establishing its potential as a valuable resource for advancing child-specific face recognition research.