A Prior Embedding-Driven Architecture for Long Distance Blind Iris Recognition
<|reference_start|>A Prior Embedding-Driven Architecture for Long Distance Blind Iris Recognition: 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.<|reference_end|>
@article{xiong2024a,
    title={A Prior Embedding-Driven Architecture for Long Distance Blind Iris
  Recognition},
    author={Qi Xiong, Xinman Zhang, Jun Shen},
    journal={arXiv preprint arXiv:2408.00210},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00210},
    primaryClass={cs.CV cs.AI}
}