Title
Cancelable iris biometrics and using Error Correcting Codes to reduce variability in biometric data
Abstract
With the increasing use of biometrics, more and more concerns are being raised about the privacy of the personal biometric data. Conventional biometric systems store biometric templates in a database. This may lead to the possibility of tracking personal information stored in one database by getting access to another database through cross-database matching. Moreover, biometric data are permanently associated with the user. Hence if stolen, they are lost permanently and become unusable in that system and possibly in all other systems based on that biometrics. In order to overcome this non-revocability of biometrics, we propose a two factor scheme to generate cancelable iris templates using iris-biometric and password. We employ a user specific shuffling key to shuffle the iris codes. Additionally, we introduce a novel way to use error correcting codes (ECC) to reduce the variabilities in biometric data. The shuffling scheme increases the impostor Hamming distance leaving genuine Hamming distance intact while the ECC reduce the Hamming distance for genuine comparisons by a larger amount than for the impostor comparisons. This results in better separation between genuine and impostor users which improves the verification performance. The shuffling key is protected by a password which makes the system truly revocable. The biometric data is stored in a protected form which protects the privacy. The proposed scheme reduces the equal error rate (EER) of the system by more than 90% (e.g., from 1.70% to 0.057% on the NIST-ICE database).
Year
DOI
Venue
2009
10.1109/CVPR.2009.5206646
Miami, FL
Keywords
Field
DocType
biometrics (access control),data privacy,database management systems,error correction codes,Hamming distance,biometric data,conventional biometric systems,cross-database matching,equal error rate,error correcting codes,iris biometrics,iris codes,iris templates,personal biometric data privacy,user specific shuffling key
Data mining,Pattern recognition,Computer science,Cryptography,Word error rate,Error detection and correction,Shuffling,Hamming distance,Artificial intelligence,Password,Biometrics,Information privacy
Conference
Volume
Issue
ISSN
2009
1
1063-6919
ISBN
Citations 
PageRank 
978-1-4244-3992-8
24
0.98
References 
Authors
12
3
Name
Order
Citations
PageRank
Sanjay Kanade1281.75
Dijana Petrovska-Delacretaz2576.98
Bernadette Dorizzi3103882.70