Original Contribution: Stacked generalization
Neural Networks
Quality-based Score Level Fusion in Multibiometric Systems
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Performance of Biometric Quality Measures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
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
Q-stack: uni- and multimodal classifier stacking with quality measures
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
An image preprocessing algorithm for illumination invariant face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
On Quality of Quality Measures for Classification
Biometrics and Identity Management
Impact of combining quality measures on biometric sample matching
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Exploiting the "Doddington Zoo" effect in biometric fusion
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Verification of aging faces using local ternary patterns and Q-stack classifier
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Gaze quality assisted automatic recognition of social contexts in collaborative Tetris
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Adult face recognition in score-age-quality classification space
BioID'11 Proceedings of the COST 2101 European conference on Biometrics and ID management
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Existing approaches to classification with signal quality measures make a clear distinction between the single- and multiple classifier scenarios. This paper presents an uniform approach to dichotomization based on the concept of stacking, Q-stack, which makes use of classindependent signal quality measures and baseline classifier scores in order to improve classification in uni- and multimodal systems alike. In this paper we demonstrate the application of Q-stack on the task of biometric identity verification using face images and associated quality measures. We show that the use of the proposed technique allows for reducing the error rates below those of baseline classifiers in single- and multiclassifier scenarios. We discuss how Q-stack can serve as a generalized framework in any single, multiple, and multimodal classifier ensemble.