GANKIN: GENERATING KIN FACES USING DISENTANGLED GAN

Abstract

Kin image generation from parents’ images is a high-level prediction and generation problem. This study presents a new method to predict and generate a kin face using parents’ faces, i.e. Tri-subject prediction or two-to-one prediction. We use a pipeline of unconditional GANs to overcome mode-collapse in conditional GANs. The model achieves promising results compared to the state-of-the-art, our model achieves a retrieval score of 0.19 versus 0.107 by the state-of-the-art. Our model is validated against SelfKin kinship verification model and achieved an accuracy of (63 % ± 7 %).