KinshipGAN: Synthesizing of Kinship Faces From Family Photos by Regularizing a Deep Face Network
This work addresses kinship face synthesis for applications in family photo analysis, but it is incremental as it builds on existing GAN and face network techniques.
The paper tackles the problem of generating child faces from parent photos by addressing dataset scarcity, and the proposed method achieves performance improvements and promising perceptual results on the Families in the Wild dataset.
In 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.