U-Net Convolution Neural Network for Multisource Heterogeneous Iris Segmentation

Accurate iris segmentation is a critical step in various applications, from biometric identification systems to ophthalmic disease diagnosis. Despite the large number of works that address this problem, iris segmentation of heterogeneous iris images acquired in different conditions is still a huge challenge. This work employed a modified U-net convolutional neural network architecture to segment iris region from heterogeneous eye images. The network was trained using the TEyeD dataset, the world's largest heterogeneous publicly available dataset of eye images.