Generation Textured Contact Lenses Iris Images Based on 4DCycle-GAN

Hang Zou, Hui Zhang, Xingguang Li, Jing Liu, Zhaofeng He

Abstract: With the development of iris recognition, many identity authentication applications began to use this inherent biometric ID. Despite the breakthroughs in the identification with iris recognition technology, one primary problem remains unsolved: the presentation spoof attack. In this paper, we present a novel algorithm 4DCycle-GAN for expanding the spoof iris image database by synthesizing fake iris images wearing textured contact lenses. The proposed 4DCycle-GAN follows the Cycle-Consistent Adversarial Networks (Cycle-GAN) framework which translating between one kind images (genuine iris images) and one other kind images (textured contact lenses iris images). The 4DCycle-GAN introduces two more discriminators to improve the Cycle-GAN at the defect of lack of diversity. The two new discriminators `prefer' images generated by the generators, while the original discriminators in Cycle-GAN `prefer' real captured images. These new added confrontations make the 4DCycle-GAN avoid generating a certain kind of contact lenses texture which is larger percentage of the training iris database. The synthesized textured contact lenses iris images are used for spoofing iris detection training to improve the robustness of classification algorithm. Both the Cycle-GAN and the 4DCycle-GAN synthesizing images can improve the spoof classification results. Moreover, by using the 4DCycle-GAN, the spoof classification results are distinctly improved for unrelated non-homologous database experiments. Extensive experimental results show that the proposed method can improve the anti-spoof ability of iris recognition system.