Experimental evaluation of expert fusion strategies
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Communications of the ACM - Multimodal interfaces that flex, adapt, and persist
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
Quality-Based Score Normalization for Audiovisual Person Authentication
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Quality-Based Score Normalization and Frame Selection for Video-Based Person Authentication
Biometrics and Identity Management
On Quality of Quality Measures for Classification
Biometrics and Identity Management
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
Improving classification with class-independent quality measures: Q-stack in face verification
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Speaker verification in score-ageing-quality classification space
Computer Speech and Language
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The use of quality measures in pattern classification has recently received a lot of attention in the areas where the deterioration of signal quality is one of the primary causes of classification errors. An example of such domain is biometric authentication. In this paper we provide a novel theoretical paradigm of using quality measures to improve both uni- and multimodal classification. We introduce Q - stack, a classifier stacking method in which feature similarity scores obtained from the first classification step are used in ensemble with the quality measures as features for the second classifier. Using two-class, synthetically generated data, we demonstrate how Q - stack helps significantly improve both uni- and multimodal classification in the presence of signal quality degradation.