@inproceedings{7e1360747ba2491188056bdc97af2d94,
title = "Super resolution reconstruction and recognition for iris image sequence",
abstract = "As a non-invasive and stable biometric identification method, iris recognition is widely used in safety certification. In large scenes or long-distance conditions, the iris images acquired may has low resolution. Lack of information in these images or videos affects the performance of the iris recognition greatly. In this paper, we proposed a scheme of super resolution to reconstruct high-resolution images from low-resolution iris image sequences. The proposed scheme applies an improved iterated back projection algorithm to reconstruct high-resolution images and does not have a restriction on the numbers of base images. We simulated our method and conducted experiments on a public database. The results show that the reconstructed high-resolution iris image provides enough pixels which contain sufficient texture information for recognition. Lower Equal Error Rate is achieved after the robust super resolution iris image reconstruction.",
keywords = "image sequence, iris recognition, reconstruction, super resolution",
author = "Huiying Ren and Yuqing He and Jing Pan and Li Li",
year = "2012",
doi = "10.1007/978-3-642-35136-5_24",
language = "English",
isbn = "9783642355059",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "193--201",
booktitle = "Biometric Recognition - 7th Chinese Conference, CCBR 2012, Proceedings",
note = "7th Chinese Conference on Biometric Recognition, CCBR 2012 ; Conference date: 04-12-2012 Through 05-12-2012",
}