Fundamentals of speech recognition
Fundamentals of speech recognition
A Cascaded Multiple Expert System for Verification
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Serial classifier combination for handwritten word recognition
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Comparison of Face Verification Results on the XM2VTS Database
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Fast features for face authentication under illumination direction changes
Pattern Recognition Letters
Large-Scale Evaluation of Multimodal Biometric Authentication Using State-of-the-Art Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Validating a Biometric Authentication System: Sample Size Requirements
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
A Novel User-Specific Face and Palmprint Feature Level Fusion
IITA '08 Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 03
A Biometric Menagerie Index for Characterising Template/Model-Specific Variation
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Exploiting the "Doddington Zoo" effect in biometric fusion
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Score normalization in multimodal biometric systems
Pattern Recognition
Serial fusion of fingerprint and face matchers
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
A Unified Framework for Biometric Expert Fusion Incorporating Quality Measures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting global and local decisions for multimodal biometrics verification
IEEE Transactions on Signal Processing - Part II
Incorporating Model-Specific Score Distribution in Speaker Verification Systems
IEEE Transactions on Audio, Speech, and Language Processing
Customizing biometric authentication systems via discriminative score calibration
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Heterogeneous information fusion: A novel fusion paradigm for biometric systems
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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The recognition performance of a biometric system varies significantly from one enrolled user to another. As a result, there is a need to tailor the system to each user. This study investigates a relatively new fusion strategy that is both user-specific and selective. By user-specific, we understand that each user in a biometric system has a different set of fusion parameters that have been tuned specifically to a given enrolled user. By selective, we mean that only a subset of modalities may be chosen for fusion. The rationale for this is that if one biometric modality is sufficiently good to recognize a user, fusion by multimodal biometrics would not be necessary, we advance the state of the art in user-specific and selective fusion in the following ways: (1) provide thorough analyses of (a) the effect of pre-processing the biometric output (prior to applying a user-specific score normalization procedure) in order to improve its central tendency and (b) the generalisation ability of user-specific parameters; (2) propose a criterion to rank the users based solely on a training score dataset in such a way that the obtained rank order will maximally correlate with the rank order that is obtained if it were to be computed on the test set; and, (3) experimentally demonstrate the performance gain of a user-specific and -selective fusion strategy across fusion data sets at different values of ''pruning rate'' that control the percentage of subjects for whom fusion is not required. Fifteen sets of multimodal fusion experiments carried out on the XM2VTS score-level benchmark database show that even though our proposed user-specific and -selective fusion strategy, its performance compares favorably with the conventional fusion system that considers all information.