Enhancing High-Resolution Facial Ageing with Dual Attention Wavelet GAN

Generating realistic facial ageing using generative adversarial networks (GANs) has seen significant progress, yet challenges such as unnatural attribute variations, image distortions, and irrelevant content modifications persist. In this paper, we introduce a dual attention mechanism and a wavelet-based discriminator within a GAN framework to capture facial attribute texture information, enhancing the ageing effect. Our approach efficiently handles both low-resolution and high-quality facial images, preserving fine details and reducing artifacts. Through extensive experiments, the effectiveness of the dual attention mechanism and wavelet-based discriminator in enhancing model performance is demonstrated. Qualitative and quantitative analyses reveal that our model achieves facial verification rates of 99.84%, 99.23%, and 98.74% for age groups 31–40, 41–50, and 51–60, respectively, with minimal discrepancies between synthesized and target age faces. Compared to existing research, our model exhibits superior performance, effectively addressing challenges in facial ageing synthesis.