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Updated: Jun 18, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Transformer-based iris verification with attention-guided segmentation and Siamese learning.

S Ramesh1, V Krishnaveni2

  • 1Department of ECE, PSG College of Technology, Coimbatore, India. rameshsivagaminathan@gmail.com.

Scientific Reports
|May 15, 2026
PubMed
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This study introduces a new AI framework for iris verification, enhancing accuracy and speed. The transformer-based system achieves high performance, making iris recognition more reliable and efficient.

Area of Science:

  • Biometrics and Pattern Recognition
  • Computer Vision
  • Artificial Intelligence

Background:

  • Iris verification is a critical biometric technology for secure identification.
  • Existing methods face challenges with image quality and complex iris textures.
  • Accurate and efficient iris recognition systems are in high demand.

Purpose of the Study:

  • To develop a unified framework for iris verification using deep learning.
  • To enhance iris image quality and segmentation accuracy.
  • To improve the discriminative power of iris feature representations.

Main Methods:

  • A transformer-based Siamese network integrating image enhancement (denoising autoencoder) and attention-guided segmentation (U-Net).
  • Global feature learning using a Vision Transformer to capture long-range dependencies in iris textures.
Keywords:
Attention-guided segmentationBiometric verificationIris recognitionMetric learningSiamese networksVision transformer

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  • Contrastive learning for optimizing feature discriminability.

    • Main Results:

    • Achieved an Equal Error Rate (EER) of 2.34% and an Area Under the ROC Curve (AUC) of 0.987 on the CASIA-IrisV3 dataset.
    • Demonstrated a True Acceptance Rate of ~95% at a False Acceptance Rate of 10⁻³.
    • Attained 97.82% verification accuracy with an average inference latency of 12.6 ms.

    Conclusions:

    • The proposed integrated framework effectively enhances iris verification performance.
    • Attention-guided segmentation and transformer-based features significantly improve accuracy and robustness.
    • The method shows consistent performance across varied near-infrared acquisition conditions.