Adversarial Iris Super Resolution

Abstract

Low resolution iris images often degrade iris recognition performance due to the lack of enough texture details. This paper proposes an adversarial iris super resolution method using a densely connected convolutional network and the adversarial learning, namely IrisDNet. The densely connected network is employed for maximum information flow between layers to achieve high iris texture reconstruction performance. An adversarial network is further incorporated into the densely connected network to sharpen texture details of iris. Moreover, for the identity persistence, we employ a pretrained network to compute an identity preserving loss to achieve semantic preserved patterns. Extensive experiments of super resolution and iris verification on multiple upscaling factors demonstrate that the proposed method achieves pleasing results with abundant high-frequency textures while maintaining identity information.