Style-exprGAN: Diverse Smile Style Image Generation Via Attention-Guided Adversarial Networks

This paper proposes a data-driven approach for generating personalized smile style images for neutral expressions, which aims to produce diverse smile styles while preserving individual features. Unlike other generator models that require expensive manual facial attribute labeling, we designed an auxiliary expression attention Siamese network (EASN) to extract identity-irrelevant facial expression attention regions and guide the proposed two-stage style-expression generative adversarial network (style-exprGAN). The first generator stage generates the overall facial geometry and virtual smile features, while the second stage refines the image quality. Additionally, we introduced traditional geometry warping methods to include registered neutral expression images for consistent transformation and realistic texture fusion. Results show that the proposed method effectively synthesizes realistic and diverse smile styles while preserving individual features. Furthermore, we demonstrate the potential of our data-driven approach by applying the generated personalized smile style images to image augmentation tasks, improving the stability and robustness of facial recognition models.