This work aims to freely translate an input face to an aging face with robust identity preservation, satisfying aging effect and authentic visual appearance. Witnessing the success of GAN in image synthesis, researchers employ GAN to address the problem of face aging synthesis. However, most GAN-based methods hold that the aging changing of all facial regions is equal, which ignores the fact that different facial regions have distinct aging speeds and aging patterns. To this end, we propose a novel Conditioned-Attention Normalization GAN (CAN-GAN) for age synthesis by leveraging the aging difference between two age groups to capture facial aging regions with different attention factors. In particular, a new Conditioned-Attention Normalization (CAN) layer is designed to enhance the aging-relevant information of face, while smoothing the aging-irrelevant information of face by attention map. Since different facial attributes contribute to the discrimination of age groups with divers degrees, we further present a Contribution-Aware Age Classifier (CAAC) that finely measures the importance of face vector’s elements in terms of the age classification. Qualitative and quantitative experiments on several commonly-used datasets show the advance of CAN-GAN compared with the other competitive methods.

这项工作旨在将输入人脸自由转换为具有稳定身份识别，满足衰老效果和真实视觉外观的衰老脸。研究人员见证了GAN在图像合成方面的成功，采用GAN来解决人脸老化合成问题。但是，大多数基于GAN的方法都认为所有面部区域的老化变化是相等的，这忽略了不同面部区域具有不同的老化速度和老化模式的事实。为此，我们通过利用两个年龄组之间的年龄差异来捕获具有不同注意因素的面部衰老区域，提出了一种用于年龄合成的新型有条件注意力归一化GAN（CAN-GAN）。特别是，设计了一个新的有条件注意力归一化（CAN）层，以增强与面部有关的衰老相关信息，同时通过注意图平滑人脸的与年龄无关的信息。由于不同的面部属性会影响具有不同程度的年龄组的区分，因此我们进一步提出了一种“贡献感知年龄分类器”（CAAC），该分类器可以根据年龄分类精细地测量面部向量元素的重要性。在几个常用数据集上进行的定性和定量实验表明，与其他竞争方法相比，CAN-GAN的进步。