A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
On combining classifiers using sum and product rules
Pattern Recognition Letters
Performance Analysis and Comparison of Linear Combiners for Classifier Fusion
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Optimal classifier combination rules for verification and identification systems
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Utilizing independence of multimodal biometric matchers
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
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There is an increased interest in the combination of biometric matchers for person verification. Matchers of different modalities can be considered as independent 2-class classifiers. This work tries to answer the question of whether assumption of the classifier independence could be used to improve the combination method. The combination added error was introduced and used to evaluate performance of various combination methods. The results show that using independence assumption for score density estimation indeed improves combination performance. At the same time it is likely that a generic classifier like SVM will still perform better. The magnitudes of experimentally evaluated combination added errors are relatively small, which means that choice of the combination method is not really important.