Despite biometrics is deemed a more secure and user-friendly solution than password-based or token-based approach for identity management, biometric templates are vulnerable to adversary attacks that may lead to privacy invasion and irreversible identity theft. Cancelable biometrics is a template protection method that generates a noninvertible identifier from the original biometric template by means of a parameterized transformation function and user/application-specific parameters. However, the necessity to input parameter, either in possession (token) or in memory (password) form along with biometrics, hence two factors, jeopardizes usability of the biometrics. In this paper, we propose a one-factor cancellable biometric authentication scheme that empowered by Indexing First Order hashing, a tailor-made locality sensitive hashing function for template protection. We evaluate the proposed scheme with respect to four template protection design criteria, namely noninvertible, renewability, unlinkability and accuracy performance. We also analyze the threat model of the proposed scheme that enclosed five major security attacks. Despite the scheme can be applied to any binary biometric features, we adopt binary fingerprint vector as a case study for this paper. The evaluations have been carried out under six datasets taken from FVC 2002 and FVC 2004 benchmark databases.
|Title of host publication||2018 24th International Conference on Pattern Recognition, ICPR 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2018 Nov 26|
|Event||24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China|
Duration: 2018 Aug 20 → 2018 Aug 24
|Name||Proceedings - International Conference on Pattern Recognition|
|Other||24th International Conference on Pattern Recognition, ICPR 2018|
|Period||18/8/20 → 18/8/24|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIP) (NO. 2016R1A2B4011656)
© 2018 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition