A prior embedding-driven architecture for long distance blind iris recognition

Abstract

Blind iris images, caused by unknown degradation during the process of iris recognition at long distances, often lead to decreased iris recognition rates. Currently, limited literature addresses this issue. To tackle this challenge, we propose a prior embedding-driven architecture for long-distance blind iris recognition. Our approach introduces a blind iris image restoration network named Iris-PPRGAN. To effectively restore the texture of blind iris images, Iris-PPRGAN incorporates a Generative Adversarial Network (GAN) as a Prior Decoder and a Deep Neural Network (DNN) as the encoder. To enhance iris feature extraction, we also developed a robust iris classifier by modifying the bottleneck module of InsightFace, referred to as Insight-Iris. Initially, a low-quality blind iris image is restored using Iris-PPRGAN, and the restored image is subsequently recognized through Insight-Iris. Experimental results on the public CASIA-Iris-distance dataset demonstrate that our method outperforms state-of-the-art blind iris restoration techniques. Specifically, the recognition rate for long-distance blind iris images improves from 80.77 % (without restoration) to 90.38 % after applying our method, reflecting an approximate ten-percentage-point improvement. These results, validated both quantitatively and qualitatively, underscore the effectiveness of our approach in addressing the challenges of long-distance blind iris recognition.