DocumentCode
3015192
Title
Revocable fingerprint biotokens: accuracy and security analysis
Author
Boult, Terrance E. ; Scheirer, Walter J. ; Woodworth, R.
Author_Institution
VAST Lab, Univ. of Colorado at Colorado Springs, Colorado Springs, CO, USA
fYear
2007
fDate
17-22 June 2007
Firstpage
1
Lastpage
8
Abstract
This paper reviews the biometric dilemma, the pending threat that may limit the long-term value of biometrics in security applications. Unlike passwords, if a biometric database is ever compromised or improperly shared, the underlying biometric data cannot be changed. The concept of revocable or cancelable biometric-based identity tokens (biotokens), if properly implemented, can provide significant enhancements in both privacy and security and address the biometric dilemma. The key to effective revocable biotokens is the need to support the highly accurate approximate matching needed in any biometric system as well as protecting privacy/security of the underlying data. We briefly review prior work and show why it is insufficient in both accuracy and security. This paper adapts a recently introduced approach that separates each datum into two fields, one of which is encoded and one which is left to support the approximate matching. Previously applied to faces, this paper uses this approach to enhance an existing fingerprint system. Unlike previous work in privacy-enhanced biometrics, our approach improves the accuracy of the underlying svstem! The security analysis of these biotokens includes addressing the critical issue of protection of small fields. The resulting algorithm is tested on three different fingerprint verification challenge datasets and shows an average decrease in the Equal Error Rate of over 30% - providing improved security and improved privacy.
Keywords
data privacy; fingerprint identification; biometric database; biometric-based identity tokens; equal error rate; fingerprint verification; privacy-enhanced biometrics; revocable fingerprint biotokens; security analysis; Biometrics; Biosensors; Data privacy; Data security; Databases; Fingerprint recognition; Government; Optical losses; Springs; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on
Conference_Location
Minneapolis, MN
ISSN
1063-6919
Print_ISBN
1-4244-1179-3
Type
conf
DOI
10.1109/CVPR.2007.383110
Filename
4270135
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