Conditional Generative Adversarial Network-Based Data Augmentation for Enhancement of Iris Recognition Accuracy

Abstract

Presently, lots of previous studies on biometrics employ convolutional neural networks (CNN) which requires a large amount of labeled training data. However, biometric data are considered as important personal information, and it is difficult to obtain large amounts of data due to individual privacy issues. Training with a small amount of data is a major cause of overfitting and low testing accuracy. To resolve this problem, previous studies have performed data augmentation that are based on geometric transforms and the adjustment of image brightness. Nevertheless, the data created by these methods have high correlation with the original data, and they cannot adequately reflect individual diversities. To resolve this problem, this study proposes iris image augmentation based on a conditional generative adversarial network (cGAN), as well as a method for improving recognition performance that uses this augmentation method. In our method, normalized iris images that are generated through arbitrary changes in the iris and pupil coordinates are used as input in the cGAN-based model to generate iris images. Due to the limitations of the cGAN model, data augmentation, which uses the periocular region, was found to fail with regard to the improvement of performance. Based on this information, only the iris region was used as input for the cGAN model. The augmentation method proposed in this paper was tested using NICE.II training dataset (selected from UBIRS.v2), MICHE database, and CASIA-Iris-Distance database. The results showed that the recognition performance was improved compared to existing studies.