IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Data-driven methods for extracting features from speech
Data-driven methods for extracting features from speech
Multimodal Biometric Authentication Using Quality Signals in Mobile Communications
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Quality-based Score Level Fusion in Multibiometric Systems
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Handbook of Biometrics
Likelihood Ratio-Based Biometric Score Fusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Thin-Plate Spline Calibration Model For Fingerprint Sensor Interoperability
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Benchmarking quality-dependent and cost-sensitive score-level multimodal biometric fusion algorithms
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
On combination of face authentication experts by a mixture of quality dependent fusion classifiers
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Quality controlled multimodal fusion of biometric experts
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Fingerprint quality indices for predicting authentication performance
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Improving fusion with margin-derived confidence in biometric authentication tasks
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Sensor interoperability and fusion in signature verification: a case study using tablet PC
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
Improving classification with class-independent quality measures: Q-stack in face verification
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Quality-based conditional processing in multi-biometrics: application to sensor interoperability
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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As biometric technology is rolled out on a larger scale, it will be a common scenario (known as cross-device matching) to have a template acquired by one biometric device used by another during testing. This requires a biometric system to work with different acquisition devices, an issue known as device interoperability. We further distinguish two subproblems, depending on whether the device identity is known or unknown. In the latter case, we show that the device information can be probabilistically inferred given quality measures (e.g., image resolution) derived from the raw biometric data. By keeping the template unchanged, cross-device matching can result in significant degradation in performance. We propose to minimize this degradation by using device-specific quality-dependent score normalization. In the context of fusion, after having normalized each device output independently, these outputs can be combined using the naive Bayes principal. We have compared and categorized several state-ofthe-art quality-based score normalization procedures, depending on how the relationship between quality measures and score is modeled, as follows: 1) direct modeling; 2) modeling via the cluster index of quality measures; and 3) extending 2) to further include the device information (device-specific cluster index). Experimental results carried out on the Biosecure DS2 data set show that the last approach can reduce both false acceptance and false rejection rates simultaneously. Furthermore, the compounded effect of normalizing each system individually in multimodal fusion is a significant improvement in performance over the baseline fusion (without using any quality information) when the device information is given.