I3FDM: IRIS Inpainting Via Inverse Fusion of Diffusion Models

Abstract

Iris images captured in real-world scenarios are often occluded, which leads to a significant degradation for the iris recognition system. Therefore, it is necessary to propose an effective iris inpainting method. While generative adversarial network (GAN)-based image inpainting methods have shown promise, they often suffer from issues such as mode collapse and training instability. Recently, denoising diffusion probabilistic model (DDPM) has surpassed GAN in terms of image quality while maintaining stable training. Combining DDPM and the characteristics of iris image, I3FDM (Iris Inpainting via Inverse Fusion of Diffusion Models), a method that iteratively modifies intermediate variables in the generation process based on a given occluded image. Since these modifications introduce semantic differences, we introduce an inverse fusion module to enhance the performance of iris inpainting. I3FDM enables the processing of various types of occluded images using an unconditional DDPM without the need for additional learning. Extensive experimental results qualitatively and quantitatively demonstrate that our method produces iris images with richer texture information and improves the performance of iris recognition.