Near Infrared Iris Image

Near-infrared (NIR) iris imaging focuses on capturing and analyzing iris patterns using NIR light, primarily for biometric identification and assessment of fitness for duty. Current research emphasizes improving image quality through super-resolution techniques, often employing deep learning architectures like convolutional neural networks and autoencoders, to address challenges posed by low-resolution images. These advancements, along with novel segmentation and classification methods (e.g., using capsule networks and attention mechanisms), aim to enhance the accuracy and robustness of iris-based systems for security and health monitoring applications. The resulting improvements in accuracy and reliability have significant implications for both biometric security and the development of automated systems for detecting impairment due to substance use or fatigue.