State-Of-The-Art Machine Learning and Deep Learning Techniques in Iris Recognition: A Review

Iris recognition has advanced remarkably from traditional handcrafted techniques to powerful deep learning–driven solutions. This review synthesizes findings from over 30 high‑impact studies to examine the evolution of iris recognition methodologies, with a focus on machine learning (ML), deep learning (DL), and hybrid frameworks. Innovations in convolutional neural networks, transformer models, generative adversarial networks, and transfer learning have enabled high accuracy (≥99%) in real-time, cross-spectral, and post-mortem scenarios. Key advancements include YOLOv4‑tiny–EfficientNet pairings for edge deployment, VGG–ResNet ensembles for spoof detection, and saliency-guided training for explainability. Foundation models like DinoV2 further enhance cross-domain generalization. Alongside technical improvements, the field has emphasized privacy through federated learning and cancelable biometrics. The review also highlights diverse applications—from PAD and liveness detection to forensic analysis—underscoring a shift toward robust, interpretable, and secure biometric systems. Overall, deep learning has redefined the iris recognition landscape, offering scalable solutions adaptable to modern security and identification needs.