Ozkan,S.Ozkan,A.Department of Electrical & Electronics Engineering2024-07-052024-07-052018978-147997061-21522-488010.1109/ICIP.2018.84513052-s2.0-85062921116https://doi.org/10.1109/ICIP.2018.8451305https://hdl.handle.net/20.500.14411/3887The Institute of Electrical and Electronics Engineers Signal Processing SocietyIn this paper, we propose a kinship generator network that can synthesize a possible child face by analyzing his/her parent's photo. For this purpose, we focus on to handle the scarcity of kinship datasets throughout the paper by proposing novel solutions in particular. To extract robust features, we integrate a pre-trained face model to the kinship face generator. Moreover, the generator network is regularized with an additional face dataset and adversarial loss to decrease the overfitting of the limited samples. Lastly, we adapt cycle-domain transformation to attain a more stable results. Experiments are conducted on Families in the Wild (FIW) dataset. The experimental results show that the contributions presented in the paper provide important performance improvements compared to the baseline architecture and our proposed method yields promising perceptual results. © 2018 IEEE.eninfo:eu-repo/semantics/openAccessFully Convolutional NetworksGenerative Adversarial NetworkKinship SynthesisConference Object2142214619