Enhancing Visible Spectrum Iris Recognition Using Hybrid Capsule Network-Attention Mechanisms

Introduction: This research presents an innovative approach to enhance visible spectrum iris recognition by integrating Capsule Networks (CapsNets) with attention mechanisms. Iris localization is achieved through Hough transforms, followed by normalization and noise reduction. Methods: Feature extraction is conducted using Capsule Networks augmented with attention mechanisms to emphasize crucial spatial regions within the iris. Extracted features are subsequently fed into a Support Vector Machine (SVM) classifier for iris classification. Additionally, data augmentation and transfer learning techniques are employed to enhance model generalization. This will encourage additional uses of this technology in banking, security, and immigration control, among other areas. The next generation of iris recognition systems should be highly accurate and robust as research progresses due to the synergy between deep learning and practical deployment techniques. Results: The efficacy of the proposed approach is evaluated through cross-validation on benchmark datasets, demonstrating promising results in terms of accuracy and robustness. While comparing the performance of different iris recognition techniques, including IrisParseNet, MADNet, ACNN-PCA, and the proposed CapsNet-AM-SVM approach. Discussion: We find that CapsNet-AM-SVM consistently outperforms other methods, achieving the highest accuracy of 97.5% with 100 test images.CapsNet-AM-SVM yields an impressive precision of 99.0% in visible spectrum iris recognition. CapsNet-AM-SVM consistently exhibits elevated recall, achieving a notable 97.0% with 100 test images. CapsNet-AM-SVM consistently demonstrates elevated F1 scores, achieving an impressive 98.0% with 100 test images. Conclusion: Thus, the results show that the proposed method accurately captures discriminative spatial features and mitigates the effects of illumination variations and occlusions.