Residual Network Deep Learning Model with Data Augmentation Effects in the Implementation of Iris Recognition - Damayanti, Farradila Ayu; Andri Asmara, Rosa; Prasetyo, Gunawan Budi (International Journal of Frontier Technology and Engineering, 2024)
Research in iris recognition using deep learning methods has gained significant traction in recent times, emerging as a robust biometric identification approach. Iris patterns encompass diverse elements like color, vascular structure, and texture, which combine uniquely and pose challenges for forgery or replication. ResNet, a specialized Deep Learning model designed for object recognition, has demonstrated remarkable performance when trained on the ImageNet Dataset. However, iris images pose a considerable challenge for recognition due to their intricate features, which are hard to discern through conventional observation or content-based feature extraction methods. This study seeks to assess the efficacy of ResNet-34 and ResNet-50 in iris recognition. Additionally, a data augmentation process will be implemented to expedite the training process and enhance accuracy. it is evident that ResNet-34 achieves a higher accuracy than ResNet-50. Specifically, ResNet-34 yields a test accuracy of 0.751 for the original dataset and 0.768 for the augmented dataset, whereas ResNet-50 achieves an accuracy of 0.73 for the original dataset and 0.747 for the augmented dataset.