Iris Segmentation

Iris segmentation, the process of isolating the iris region from an eye image, is crucial for iris recognition systems used in security and biometric identification. Current research emphasizes improving segmentation accuracy and robustness using deep learning architectures like U-Net, often incorporating pre-trained models such as MobileNetV2 and leveraging techniques like focal loss to address class imbalances and data augmentation to handle variations in pupil dilation. These advancements lead to more reliable iris recognition, impacting applications ranging from forensic identification to livestock traceability, with a focus on improving speed and accuracy even in challenging conditions like poor image quality or post-mortem analysis.