Eye Recognition With Mixed Convolutional and Residual Network (MiCoRe-Net)

Although iris recognition has achieved big successes on biometric identification in recent years, difficulties in the collection of iris images with high resolution and in the segmentation of valid regions prevent it from applying to large-scale practical applications. In this paper, we present an eye recognition framework based on deep learning, which relaxes the data collection procedure, improves the anti-fake quality, and promotes the performance of biometric identification. Specifically, we propose and train a mixed convolutional and residual network (MiCoRe-Net) for the eye recognition task. Such an architecture inserts a convolutional layer between every two residual layers and takes the advantages from both of convolutional networks and residual networks. Experiment results show that the proposed approach achieves accuracies of 99.08% and 96.12% on the CASIA-Iris-IntervalV4 and the UBIRIS.v2 datasets, respectively, which outperforms other classical classifiers and deep neural networks with other architectures.