Generative adversarial networks (GANs) have recently achieved impressive results in facial age synthesis. However, these methods usually select an autoencoder-style generator. And the bottleneck layer in the encoder-decoder generally gives rise to blurry and low-quality generation. To address this limitation, we propose a novel attention-aware conditional generative adversarial network (ACGAN). First, we utilize two different attention mechanisms to improve generation quality. On one hand, we integrate channel attention modules into the generator to enhance the discriminative representation power. On the other hand, we introduce a position attention mask to well-process images captured with various backgrounds and illuminations. Second, we deploy a local discriminator to enhance the central face region with informative details. Third, we adopt three types of losses to achieve accurate age generation and preserve personalized features: 1) The adversarial loss aims to synthesize photo-realistic faces with expected aging effects; 2) The identity loss intends to keep identity information unchanged; 3) The attention loss tries to improve the accuracy of attention mask regression. To assess the effectiveness of the proposed method, we conduct extensive experiments on several public aging databases. Experimental results on MORPH, CACD, and FG-NET show the effectiveness of the proposed framework.

生成对抗网络（GAN）最近在面部年龄合成中取得了令人印象深刻的结果。但是，这些方法通常选择自动编码器样式的生成器。并且，编码器/解码器中的瓶颈层通常会导致模糊和低质量的生成。为了解决这一局限性，我们提出了一种新颖的注意力感知条件生成对抗网络（ACGAN）。首先，我们利用两种不同的注意力机制来提高发电质量。一方面，我们将频道关注模块集成到生成器中，以增强区分性表示能力。另一方面，我们引入了位置注意遮罩，以很好地处理在各种背景和光照下捕获的图像。其次，我们部署了一个本地鉴别器，以提供更多信息，从而增强了中央面部区域。第三，我们采用三种类型的损失来实现准确的年龄生成并保留个性化功能：1）对抗性损失旨在合成具有预期衰老效果的逼真的面部；2）身份丢失旨在保持身份信息不变；3）注意力损失试图提高注意力模板回归的准确性。为了评估该方法的有效性，我们在几个公共老化数据库上进行了广泛的实验。在MORPH，CACD和FG-NET上的实验结果表明了该框架的有效性。3）注意力损失试图提高注意力模板回归的准确性。为了评估该方法的有效性，我们在几个公共老化数据库上进行了广泛的实验。在MORPH，CACD和FG-NET上的实验结果表明了该框架的有效性。3）注意力损失试图提高注意力模板回归的准确性。为了评估该方法的有效性，我们在几个公共老化数据库上进行了广泛的实验。在MORPH，CACD和FG-NET上的实验结果表明了该框架的有效性。