  We address the need for a large-scale database of children's faces by using
generative adversarial networks (GANs) and face age progression (FAP) models to
synthesize a realistic dataset referred to as HDA-SynChildFaces. To this end,
we proposed a processing pipeline that initially utilizes StyleGAN3 to sample
adult subjects, which are subsequently progressed to children of varying ages
using InterFaceGAN. Intra-subject variations, such as facial expression and
pose, are created by further manipulating the subjects in their latent space.
Additionally, the presented pipeline allows to evenly distribute the races of
subjects, allowing to generate a balanced and fair dataset with respect to race
distribution. The created HDA-SynChildFaces consists of 1,652 subjects and a
total of 188,832 images, each subject being present at various ages and with
many different intra-subject variations. Subsequently, we evaluates the
performance of various facial recognition systems on the generated database and
compare the results of adults and children at different ages. The study reveals
that children consistently perform worse than adults, on all tested systems,
and the degradation in performance is proportional to age. Additionally, our
study uncovers some biases in the recognition systems, with Asian and Black
subjects and females performing worse than White and Latino Hispanic subjects
and males.
