Paper Info
Title
Iris Template Protection Based on Local Ranking.
Abstract
Biometrics have been widely studied in recent years, and they are increasingly employed in real-world applications. Meanwhile, a number of potential threats to the privacy of biometric data arise. Iris template protection demands that the privacy of iris data should be protected when performing iris recognition. According to the international standard ISO/IEC 24745, iris template protection should satisfy the irreversibility, revocability, and unlinkability. However, existing works about iris template protection demonstrate that it is difficult to satisfy the three privacy requirements simultaneously while supporting effective iris recognition. In this paper, we propose an iris template protection method based on local ranking. Specifically, the iris data are first XORed (Exclusive OR operation) with an application-specific string; next, we divide the results into blocks and then partition the blocks into groups. The blocks in each group are ranked according to their decimal values, and original blocks are transformed to their rank values for storage. We also extend the basic method to support the shifting strategy and masking strategy, which are two important strategies for iris recognition. We demonstrate that the proposed method satisfies the irreversibility, revocability, and unlinkability. Experimental results on typical iris datasets (i.e., CASIA-IrisV3-Interval, CASIA-IrisV4-Lamp, UBIRIS-V1-S1, and MMU-V1) show that the proposed method could maintain the recognition performance while protecting the privacy of iris data.
Year
DOI
Venue
2018
10.1155/2018/4519548
SECURITY AND COMMUNICATION NETWORKS
Field
DocType
Volume
Iris recognition,Ranking,Pattern recognition,Computer science,Exclusive or,Computer security,Artificial intelligence,Biometrics,Iris flower data set,Biometric data,Decimal
Journal
2018
ISSN
Citations 
PageRank 
1939-0114
0
0.34
References 
Authors
14
5
Name
Order
Citations
PageRank
Dongdong Zhao13420.62
Shu Fang2102.90
Jianwen Xiang311323.36
Jing Tian473.43
Shengwu Xiong518953.59