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Kinshipgan: Synthesizing of Kinship Faces from Family Photos by Regularizing a Deep Face Network

机译:亲属事件:通过规范深脸网络来综合家庭照片的亲属面孔

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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.
机译:在本文中,我们提出了一种亲属发生器网络,可以通过分析他/她的父母的照片来综合可能的儿童面部。为此目的,我们专注于通过提出新颖的解决方案来处理整个纸张中的亲属数据集的稀缺性。为了提取稳健的功能,我们将预先训练的面部模型整合到血缘关系面部发生器。此外,通过额外的面部数据集和越野丢失来规范发电机网络,以降低有限样品的过度拟合。最后,我们适应周期域变换以获得更稳定的结果。实验在野生(FIW)数据集中的家庭上进行。实验结果表明,与基线架构相比,本文提出的贡献提供了重要的性能改进,我们提出的方法产生了有希望的感知结果。

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