Verification system based on long-range iris and Graph Siamese Neural Networks

TL;DR

This paper introduces a novel iris verification system using long-range images converted into graphs and classified with Graph Siamese Neural Networks, addressing hardware and proximity limitations of traditional biometric systems.

Contribution

The work presents a new methodology for converting long-range iris images into graphs and applying Graph Siamese Neural Networks for verification, with analysis of spectral components for improved accuracy.

Findings

Effective verification with long-range iris images.

Graph-based approach shows promising results.

Spectral components enhance classification performance.

Abstract

Biometric systems represent valid solutions in tasks like user authentication and verification, since they are able to analyze physical and behavioural features with high precision. However, especially when physical biometrics are used, as is the case of iris recognition, they require specific hardware such as retina scanners, sensors, or HD cameras to achieve relevant results. At the same time, they require the users to be very close to the camera to extract high-resolution information. For this reason, in this work, we propose a novel approach that uses long-range (LR) distance images for implementing an iris verification system. More specifically, we present a novel methodology for converting LR iris images into graphs and then use Graph Siamese Neural Networks (GSNN) to predict whether two graphs belong to the same person. In this study, we not only describe this methodology but also evaluate how the spectral components of these images can be used for improving the graph extraction and the final classification task. Results demonstrate the suitability of this approach, encouraging the community to explore graph application in biometric systems.