CCS '99 Proceedings of the 6th ACM conference on Computer and communications security
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
FVC2000: Fingerprint Verification Competition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhancing security and privacy in biometrics-based authentication systems
IBM Systems Journal - End-to-end security
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Face Recognition with Renewable and Privacy Preserving Binary Templates
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
Designs, Codes and Cryptography
Pseudo Identities Based on Fingerprint Characteristics
IIH-MSP '08 Proceedings of the 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing
Intersubject differences in false nonmatch rates for a fingerprint-based authentication system
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
New shielding functions to enhance privacy and prevent misuse of biometric templates
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Practical biometric authentication with template protection
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Fingerprint-Based Fuzzy Vault: Implementation and Performance
IEEE Transactions on Information Forensics and Security
Likelihood-ratio-based biometric verification
IEEE Transactions on Circuits and Systems for Video Technology
"3D Face": biometric template protection for 3d face recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Robust extraction of secret bits from minutiae
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Fast and Accurate 3D Face Recognition
International Journal of Computer Vision
A quantitative analysis of indistinguishability for a continuous domain biometric cryptosystem
DPM'09/SETOP'09 Proceedings of the 4th international workshop, and Second international conference on Data Privacy Management and Autonomous Spontaneous Security
Biometric cryptosystem based on discretized fingerprint texture descriptors
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
In recent years, the protection of biometric data has gained increased interest from the scientific community. Methods such as the fuzzy commitment scheme, helper-data system, fuzzy extractors, fuzzy vault, and cancelable biometrics have been proposed for protecting biometric data. Most of these methods use cryptographic primitives or error-correcting codes (ECCs) and use a binary representation of the real-valued biometric data. Hence, the difference between two biometric samples is given by the Hamming distance (HD) or bit errors between the binary vectors obtained from the enrollment and verification phases, respectively. If the HD is smaller (larger) than the decision threshold, then the subject is accepted (rejected) as genuine. Because of the use of ECCs, this decision threshold is limited to the maximum error-correcting capacity of the code, consequently limiting the false rejection rate (FRR) and false acceptance rate tradeoff. A method to improve the FRR consists of using multiple biometric samples in either the enrollment or verification phase. The noise is suppressed, hence reducing the number of bit errors and decreasing the HD. In practice, the number of samples is empirically chosen without fully considering its fundamental impact. In this paper, we present a Gaussian analytical framework for estimating the performance of a binary biometric system given the number of samples being used in the enrollment and the verification phase. The error-detection tradeoff curve that combines the false acceptance and false rejection rates is estimated to assess the system performance. The analytic expressions are validated using the Face Recognition Grand Challenge v2 and Fingerprint Verification Competition 2000 biometric databases.