  Blind iris images, which result from unknown degradation during the process
of iris recognition at long distances, often lead to decreased iris recognition
rates. Currently, little existing literature offers a solution to this problem.
In response, we propose a prior embedding-driven architecture for long distance
blind iris recognition. We first proposed a blind iris image restoration
network called Iris-PPRGAN. To effectively restore the texture of the blind
iris, Iris-PPRGAN includes a Generative Adversarial Network (GAN) used as a
Prior Decoder, and a DNN used as the encoder. To extract iris features more
efficiently, we then proposed a robust iris classifier by modifying the
bottleneck module of InsightFace, which called Insight-Iris. A low-quality
blind iris image is first restored by Iris-PPRGAN, then the restored iris image
undergoes recognition via Insight-Iris. Experimental results on the public
CASIA-Iris-distance dataset demonstrate that our proposed method significantly
superior results to state-of-the-art blind iris restoration methods both
quantitatively and qualitatively, Specifically, the recognition rate for
long-distance blind iris images reaches 90% after processing with our methods,
representing an improvement of approximately ten percentage points compared to
images without restoration.
