Abstract

Introduction: Visible spectrum iris recognition is an essential component of biometric identification systems since it offers robust security measures. This approach makes use of Transformer Networks, which are well-known for their powerful attention mechanisms.

Methods: In this study, the novel AHE-TAM method proposed for iris recognition in the visible spectrum was presented, and the application of Transformer Networks was investigated. When compared to earlier methods, AHE-TAM offers significant improvements in terms of precision, safety, and efficiency of computing. Through the utilization of attention mechanisms, the model can adapt to intricate details on the fly, thereby surpassing the performance of AHE-CNN, AHETransformerNet, and AHE-AM by an impressive margin of 1% on average. AHE-TAM also has improved security because it reduces the average FAR by 8% and the FRR by 7%.

Results: This results in a lower overall risk. ROC AUC values have improved by 4%, which is a significant improvement that highlights the improved discriminatory power. Conclusion: The use of AHE-TAM results in a reduction of the computational processing times by an average of 13%.

Keywords: Iris recognition, visible spectrum, biometrics, transformer networks, feature extraction, attention mechanism.