A Comparative Study of Image-to-Image Translation Using GANs for
  Synthetic Child Race Data
Wang Yao Muhammad Ali Farooq Joseph Lemley Peter Corcoran
The lack of ethnic diversity in data has been a limiting factor of face
recognition techniques in the literature. This is particularly the case for
children where data samples are scarce and presents a challenge when seeking to
adapt machine vision algorithms that are trained on adult data to work on
children. This work proposes the utilization of image-to-image transformation
to synthesize data of different races and thus adjust the ethnicity of
children's face data. We consider ethnicity as a style and compare three
different Image-to-Image neural network based methods, specifically pix2pix,
CycleGAN, and CUT networks to implement Caucasian child data and Asian child
data conversion. Experimental validation results on synthetic data demonstrate
the feasibility of using image-to-image transformation methods to generate
various synthetic child data samples with broader ethnic diversity.