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

TL;DR

This paper introduces a novel architecture combining iris image restoration and feature extraction techniques to improve recognition rates of long-distance blind iris images, achieving significant accuracy improvements.

Contribution

The paper presents Iris-PPRGAN for iris image restoration and Insight-Iris for feature extraction, advancing long-distance blind iris recognition methods.

Findings

Recognition rate reaches 90% after processing.

Significant improvement over state-of-the-art methods.

Effective restoration of iris textures in degraded images.

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.