A simple unpredictable pseudo random number generator
SIAM Journal on Computing
Floating search methods in feature selection
Pattern Recognition Letters
FVC2000: Fingerprint Verification Competition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Thermal Face Recognition Over Time
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Biometric Systems: Technology, Design and Performance Evaluation
Biometric Systems: Technology, Design and Performance Evaluation
Personal authenticator on the basis of two-factors: palmprint features and tokenized random data
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Remarks on BioHash and its mathematical foundation
Information Processing Letters
Cancellable biometrics and annotations on BioHash
Pattern Recognition
Local binary patterns for a hybrid fingerprint matcher
Pattern Recognition
Input Decimated Ensemble based on Neighborhood Preserving Embedding for spectrogram classification
Expert Systems with Applications: An International Journal
Fusion of color spaces for ear authentication
Pattern Recognition
Creating ensembles of classifiers via fuzzy clustering and deflection
Fuzzy Sets and Systems
Cancelable fingerprint templates using minutiae-based bit-strings
Journal of Network and Computer Applications
PalmHash Code vs. PalmPhasor Code
Neurocomputing
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Given the recent explosion of interest in human authentication, verification based on tokenized pseudo-random numbers and the user-specific biometric feature has received much attention. These methods have significant functional advantages over solely biometrics, i.e. zero equal error rate. The main drawback of the methods proposed in the literature relies in exhibiting low performance when an ''impostor''B steals the pseudo-random numbers of A and he tries to authenticate as A. In this paper, we show that a multimodal fusion, where only one biometric characteristic is combined with the pseudo-random numbers, permits to obtain a zero equal error rate when nobody steals the pseudo-random numbers, and good performance when an ''impostor''B steals the pseudo-random numbers of A and he tries to authenticate as A. In this paper, we study the fusion among the score obtained by a Face Recognizer (where the face features are combined with pseudo-random numbers) and the scores of the systems submitted to FVC2004.