DiffAtten-GAN: A Local Attention-Enhanced Diffusion-GAN for Elderly Facial Expression Synthesis with Limited Data

The rapid aging of the global population has heightened the demand for sophisticated emotion recognition systems in elderly care, yet the limited availability of comprehensive elderly facial expression datasets remains a significant barrier. In this study, we introduce DiffAtten-GAN, a novel generative model that seamlessly integrates a local attention mechanism with a Diffusion-GAN framework to synthesize high-fidelity elderly facial expressions from limited data. Leveraging a diverse dataset of 6,311 images obtained from FairFace [1] and UTKFace [2], our approach effectively captures age-specific features and subtle emotional cues across varied ethnicities. Experimental results demonstrate that DiffAtten-GAN outperforms conventional GAN architectures, achieving a Fréchet Inception Distance (FID) of 10.423 and an Inception Score (IS) of 2.761 -corresponding to improvements of $21.26 \%$ and $10.09 \%$, respectively, over a baseline StyleGAN2 model. This work addresses data scarcity in elderly expression analysis and contributes a novel hybrid architecture, a local attention module, significant FID/IS improvements, and comprehensive evaluations including human and cross-dataset studies.