Generative Iris Prior Embedded Transformer for Iris Restoration
This addresses iris recognition enhancement for security applications, representing a domain-specific incremental improvement.
The paper tackles iris restoration from degraded images to improve iris recognition by proposing Gformer, a generative iris prior embedded Transformer model that outperforms state-of-the-art methods and significantly boosts recognition performance.
Iris restoration from complexly degraded iris images, aiming to improve iris recognition performance, is a challenging problem. Due to the complex degradation, directly training a convolutional neural network (CNN) without prior cannot yield satisfactory results. In this work, we propose a generative iris prior embedded Transformer model (Gformer), in which we build a hierarchical encoder-decoder network employing Transformer block and generative iris prior. First, we tame Transformer blocks to model long-range dependencies in target images. Second, we pretrain an iris generative adversarial network (GAN) to obtain the rich iris prior, and incorporate it into the iris restoration process with our iris feature modulator. Our experiments demonstrate that the proposed Gformer outperforms state-of-the-art methods. Besides, iris recognition performance has been significantly improved after applying Gformer.