Verification system based on long-range iris and Graph Siamese Neural Networks
This addresses the need for less intrusive, hardware-flexible biometric verification for users, though it appears incremental as it adapts existing graph and neural network methods to a specific domain.
The paper tackles the problem of iris verification at long distances by converting long-range iris images into graphs and using Graph Siamese Neural Networks for prediction, achieving results that demonstrate the approach's suitability and encouraging further exploration in biometric systems.
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.