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

Qi Xiong; Xinman Zhang; Jun Shen

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arXiv · 2408.00210

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

Qi Xiong·Xinman Zhang·Jun Shen

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Abstract

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.

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BibTeX RIS

Qi Xiong, Xinman Zhang, Jun Shen. 2024-08-01. A Prior Embedding-Driven Architecture for Long Distance Blind Iris Recognition. https://arxiv.org/abs/2408.00210

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