Super resolution reconstruction and recognition for iris image sequence

Authors:
Huiying Ren;Yuqing He;Jing Pan;Li Li
Affiliations:
Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, School of Optoelectronics, Beijing Institute of Technology, Beijing, China;Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, School of Optoelectronics, Beijing Institute of Technology, Beijing, China;Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, School of Optoelectronics, Beijing Institute of Technology, Beijing, China;Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, School of Optoelectronics, Beijing Institute of Technology, Beijing, China
Venue:
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Year:
2012

Citing 5
Cited 0

A frequency domain approach to registration of aliased images with application to super-resolution

EURASIP Journal on Applied Signal Processing
Image Averaging for Improved Iris Recognition

ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Robust mean super-resolution for less cooperative NIR iris recognition at a distance and on the move

Proceedings of the 2010 Symposium on Information and Communication Technology
Information fusion in low-resolution iris videos using Principal Components Transform

WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
How iris recognition works

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

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.