Dynamic U-Net Using Residual Network for Iris Segmentation

Abstract

Biometric applications have taken tremendous attention these days due to technological advancements and the high demand for safety and security systems. Regardless of the existing biometric traits such as fingerprints, palm, face, retina, voice, and gait, the iris is known as the most consistent and precise trait. Iris segmentation is the most significant and essential stage in the iris recognition process. The segmentation method is precisely related to the performance accuracy of iris recognition. In this study, we proposed a Dynamic U-Net using ResNet-34 to improve the segmentation results based on the F1 score. The proposed method would produce a better accuracy on the condition of applying post-processing. However, based on the comparative analysis with other methods in the literature, our proposed method has produced a higher F1 score. The segmentation results were compared with the Unified IrisParseNet. Our proposed method has produced 93.66% accuracy, which higher than Unified IrisParseNet at 93.05%, respectively. The computational time is also high, which can be further improved in future work.