Face Aging Synthesis by Deep Cycle Generative Adversarial Networks and Bias Loss

In this work, we propose a deep learning-based adversarial network for human face aging synthesis. We use an image dataset consisting of young (Domain X) and elderly (Domain Y) individuals to generate aged images at various target ages. Our network employs two adversarial components: a generator that synthesizes aged images and a discriminator that verifies their authenticity. Through adversarial training, the generator iteratively improves its output based on feedback from the discriminator, ultimately achieving a Nash equilibrium where the generated images closely match the target ages. Moreover, we integrate deep ResNet blocks in the generator, with the best image quality achieved using a 7-layer configuration. To address artifacts caused by overly strict discriminators, we introduce a novel bias loss function that relaxes the discriminator's harshness, resulting in more realistic aging effects. Additionally, we incorporate perceptual loss using a pre-trained VGG16 network to preserve identity features during age progression. Experimental results show that the proposed architecture significantly enhances aging realism and identity preservation, outperforming existing models both qualitatively and quantitatively. The source code and pre-trained models are available at (https://github.com/Tony00728/FAS-by-CGAN-and-BiasLoss).