# A Prior Embedding-Driven Architecture for Long Distance Blind Iris Recognition

arXiv: https://arxiv.org/abs/2408.00210 · HTML: https://mlsanity.com/paper/2408.00210/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 11 of 11)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models](https://mlsanity.com/paper/2003.03808/) (2020)
- [GAN Prior Embedded Network for Blind Face Restoration in the Wild](https://mlsanity.com/paper/2105.06070/) (2021)
- [Dual Aggregation Transformer for Image Super-Resolution](https://mlsanity.com/paper/2308.03364/) (2023)
- [Progressive Semantic-Aware Style Transformation for Blind Face Restoration](https://mlsanity.com/paper/2009.08709/) (2020)
- [HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment](https://mlsanity.com/paper/2005.05005/) (2020)
- [Iris super-resolution using CNNs: is photo-realism important to iris recognition?](https://mlsanity.com/paper/2210.13125/) (2022)

## Abstract (arXiv)

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.