Iris Image Super Resolution Based on GANs with Adversarial Triplets

Xiao Wang, Hui Zhang, Jing Liu, Lihu Xiao, Zhaofeng He, Liang Liu, Pengrui Duan

Abstract: Iris recognition is a safe and reliable biometric technology commonly used at present. However, due to the limitations of equipment and environment in a variety of application scenarios, the obtained iris image may be of low quality and not clear enough. In recent years, there are many attempts to apply neural networks to iris image enhancement. This paper is inspired by SRGAN, and introduces the adversarial idea into the triplet network, finally proposing a novel iris image super-resolution architecture. With triplet loss, the Network can keep reducing intra-class distance and expanding inter-class distance during iris image reconstruction. The experiments on CASIA’s several benchmark iris image datasets yield considerable results. This architecture makes a contribution to enhancing iris images for recognition.