KinshipGAN：通过正则化深层人脸网络从家庭照片合成亲属人脸
KinshipGAN: Synthesizing of Kinship Faces From Family Photos by Regularizing a Deep Face Network
摘要：
在本文中，我们提出了一个亲属生成器网络，可以合成一个可能的孩子的脸，通过分析他/她的父母的照片。为此，我们专注于处理亲属数据集的稀缺性，特别是提出新的解决方案。为了提取鲁棒的特征，我们将预先训练的人脸模型集成到亲属人脸生成器中。此外，生成器网络使用额外的人脸数据集和对抗性损失进行正则化，以减少有限样本的过拟合。最后，我们采用循环域变换，以获得更稳定的结果。实验是在野生家庭（FIW）数据集上进行的。实验结果表明，本文提出的贡献提供了重要的性能改进相比，基线架构和我们提出的方法产生有前途的感知结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
