@inproceedings{bc58eacce81f49ab9e96c566b7aafe46,
title = "Iris Image Super Resolution Based on GANs with Adversarial Triplets",
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{\textquoteright}s several benchmark iris image datasets yield considerable results. This architecture makes a contribution to enhancing iris images for recognition.",
keywords = "Biometric technology, GANs, Iris image super-resolution, Triplet network",
author = "Xiao Wang and Hui Zhang and Jing Liu and Lihu Xiao and Zhaofeng He and Liang Liu and Pengrui Duan",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 14th Chinese Conference on Biometric Recognition, CCBR 2019 ; Conference date: 12-10-2019 Through 13-10-2019",
year = "2019",
doi = "10.1007/978-3-030-31456-9\_39",
language = "英语",
isbn = "9783030314552",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "346--353",
editor = "Zhenan Sun and Ran He and Shiguang Shan and Jianjiang Feng and Zhenhua Guo",
booktitle = "Biometric Recognition - 14th Chinese Conference, CCBR 2019, Proceedings",
address = "德国",
}