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- Title
Cancellable biometrics based on the index-of-maximum hashing with random sparse binary encoding.
- Authors
Kim, Jihyeon; Park, Jaewoo; Low, Cheng Yaw; Teoh, Andrew Beng Jin
- Abstract
The wide deployment of biometrics has prompted enormous security and privacy concerns regarding biometric template protection. Cancellable biometrics is one of the most representative countermeasures that maps a biometric feature vector into its protected template non-invertible yet replaceable when the previously transformed template is compromised. This paper proposes a two-stage cancellable biometric scheme on top of the Index-of-Maximum (IoM) hashing, dubbed Random Sparse Binary Encoded IoM (RSBE-IoM) hashing. Unlike the vanilla IoM hashing that operates only on the first-ranked feature indices, the RSBE-IoM hashing employs the first and the second-ranked features for additional discriminability gain. We also devise an instance-specific Random Sparse Binary (RSB) encoder to project the IoM-hashed vectors (both first and second-ranked) into a random sparse binary bitstring. We also introduce an instance-specific seeding mechanism to the RSB encoder such that a one-time seed is generated on the fly and not physically cached upon enrolment. We comprehensively evaluate the proposed RSBE-IoM scheme with six fingerprint benchmark datasets, i.e., DB 1 to 3 for each FVC 2002 and FVC 2004. In addition to accuracy performance, our discussions cover other important perspectives, including computational cost, biometric template protection criteria, and privacy and security concerns.
- Subjects
BIOMETRY; ENCODING; HUMAN fingerprints; VANILLA; PRIVACY
- Publication
Multimedia Tools & Applications, 2024, Vol 83, Issue 21, p59915
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-023-17711-w