The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition with Renewable and Privacy Preserving Binary Templates
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Remarks on BioHash and its mathematical foundation
Information Processing Letters
Eigenfeature Regularization and Extraction in Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cancellable biometrics and annotations on BioHash
Pattern Recognition
EURASIP Journal on Advances in Signal Processing
Biometric quantization through detection rate optimized bit allocation
EURASIP Journal on Advances in Signal Processing
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
IEEE Transactions on Information Forensics and Security
Practical biometric authentication with template protection
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
IEEE Transactions on Information Theory
Editorial: High performance biometrics recognition algorithms and systems
Future Generation Computer Systems
Biometric cryptosystem based on discretized fingerprint texture descriptors
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
In this paper, a dynamic biometric discretization scheme based on Linnartz and Tuyls's quantization index modulation scheme (LT-QIM) [Linnartz and Tuyls, 2003] is proposed. LT-QIM extracts one bit per feature element and takes care of the intra-class variation of the biometric features. Nevertheless, LT-QIM does not consider statistical distinctiveness between users, and thus lacks the capability of preserving the discriminative power of the original biometric features. We put forward a generalized LT-QIM scheme in such a way that it allocates multiple bits to each feature element according to a statistical distinctiveness measure of the feature. Hence, more bits are assigned to high distinctive features and fewer bits to low distinctive features. With provision for intra-class variation compensation and dynamic bit allocation by means of the statistical distinctiveness measure, the generalized scheme enhances the verification performance compared to the original scheme. Several comparative studies are conducted on two popular face data sets to justify the efficiency and feasibility of our proposed scheme. The security aspect is also considered by including a stolen-token scenario.