Iris Recognition Based Deep Learning: A Survey
DOI:
https://doi.org/10.65542/djei.v2i1.17Keywords:
Iris Recognition, Deep Learning, Convolutional Neural Network, Transfer Learning, BiometricsAbstract
The iris recognition system is one of the most reliable biometric authentication systems owing to its individuality and permanence. With the introduction of deep learning concepts, especially CNNs, iris recognition models have improved tremendously in accuracy, robustness, and efficiency. We present an extensive survey summ2arizing the application of deep learning in iris recognition; covering datasets, feature extraction, architectures, and evaluation metrics. We conclude that while CNNs and transfer learning produce the best accuracy on well-constrained datasets, serious challenges lie with cross-sensor generalization and robustness under mobile or unconstrained environments. The new solutions that are generating attention, such as generation adversarial network-based augmentation and attention-driven architectures, are solution pathways to surmount data scarcity and further strengthen the adaptation capability. This survey is aimed at steering researchers and practitioners to various critical challenges and directions that have the most potential for future iris recognition systems.
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