Face aging and rejuvenating work effectively in public security criminal investigation, cross-age recognition, and entertainment. However, three main problems still exist: the lack of accurate and sufficient dataset, low aging effect, and poor preservation of personal information. We propose a semi-supervised face aging and rejuvenating method for face aging and rejuvenating. In particular, a conditional encoder is utilized to map an input face into a latent vector, which is used by the generator network with age conditions to produce a new face. The latent vector preserves identity information, whereas the age label controls face aging or rejuvenating. To make generated features closer to prior features, the discriminator network is designed to assist the generator network. In addition, a cycle optimized method is utilized to preserve the personal information of the generated face. Experimental results demonstrate that our network can generate more realistic faces, both in personal identity and age consistency.

中文翻译：

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半监督脸部衰老和焕发青春

在公安刑事调查，跨年龄识别和娱乐方面有效面对衰老和振兴工作。但是，仍然存在三个主要问题：缺乏准确和足够的数据集，老化效果低以及个人信息的保存不充分。我们提出了一种半监督的面部老化和复兴方法，用于面部老化和复兴。特别地，条件编码器用于将输入面部映射到潜矢量，发电机网络将其与年龄条件一起使用以产生新面部。潜在向量保留身份信息，而年龄标签则控制脸部衰老或恢复青春活力。为了使生成的特征更接近于先前的特征，鉴别器网络被设计为辅助生成器网络。此外，利用循环优化方法来保存所生成脸部的个人信息。实验结果表明，我们的网络可以在个人身份和年龄一致性方面生成更真实的面孔。