Transformer-based iris verification with attention-guided segmentation and Siamese learning

This study presents a transformer-based Siamese framework for iris verification that integrates image enhancement, attention-guided segmentation, and global feature learning within a unified pipeline. A deep denoising autoencoder is employed to improve image quality, followed by an attention-guided U-Net for precise iris localization. Global feature representations are extracted using a Vision Transformer, enabling the modeling of long-range dependencies across iris textures, and are optimized through contrastive learning to enhance discriminability. The proposed framework is evaluated on the CASIA-IrisV3 dataset, including the Interval, Lamp, and Twins subsets, under a subject-disjoint verification protocol. Experimental results achieve an Equal Error Rate (EER) of 2.34% and an Area Under the ROC Curve (AUC) of 0.987, with a True Acceptance Rate of approximately 95% at a False Acceptance Rate of 10⁻³. The method attains a verification accuracy of 97.82% while maintaining efficient inference with an average latency of 12.6 ms per comparison. These results indicate consistent performance across varying acquisition conditions within the near-infrared domain, demonstrating the effectiveness of integrating attention-guided segmentation and transformer-based representations for iris verification.