IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining multiple matchers for a high security fingerprint verification system
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Expert Conciliation for Multi Modal Person Authentication Systems by Bayesian Statistics
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Multimodal Biometric Authentication Using Quality Signals in Mobile Communications
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Fusion strategies in multimodal biometric verification
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Quality-based Score Level Fusion in Multibiometric Systems
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Unbiased assessment of learning algorithms
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Fusion of biometric algorithms in the recognition problem
Pattern Recognition Letters
Fingerprint quality indices for predicting authentication performance
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
A principled approach to score level fusion in multimodal biometric systems
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
A score-level fusion benchmark database for biometric authentication
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Threshold-optimized decision-level fusion and its application to biometrics
Pattern Recognition
Decision dependability and its application to identity management
Proceedings of the 5th Annual Workshop on Cyber Security and Information Intelligence Research: Cyber Security and Information Intelligence Challenges and Strategies
Score Fusion by Maximizing the Area under the ROC Curve
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Benchmarking quality-dependent and cost-sensitive score-level multimodal biometric fusion algorithms
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
Estimating and fusing quality factors for iris biometric images
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent 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
Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation
Computer Vision and Image Understanding
An efficient algorithm for multi-focus image fusion using PSO-ICA
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Applied Computational Intelligence and Soft Computing
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Biometric systems for today's high security applications must meet stringent performance requirements; fusing multiple biometrics can help lower system error rates. Fusion methods include processing biometric modalities sequentially until an acceptable match is obtained, using logical (AND/OR) operations, or summing similarity scores. More sophisticated methods combine scores from separate classifiers for each modality. This paper develops a novel fusion architecture based on Bayesian belief networks. Although Bayesian update methods have been used before, our approach more fully exploits the graphical structure of Bayes nets to define and explicitly model statistical dependencies between relevant variables: per sample measurements, such as match scores and corresponding quality estimates, and global decision variables. These statistical dependencies are in the form of conditional distributions which we model as Gaussian, gamma, log-normal or beta, each of which is determined by its mean and variance, thus significantly reducing training data requirements. Moreover, by conditioning decision variables on quality as well as match score, we can extract information from lower quality measurements rather than rejecting them out of hand. Another feature of our method is a global quality measure designed to be used as a confidence estimate supporting decision making. Preliminary studies using the architecture to fuse fingerprints and voice are reported.