IEEE Transactions on Pattern Analysis and Machine Intelligence
Using independence assumption to improve multimodal biometric fusion
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Cluster Ensembles Based on Vector Space Embeddings of Graphs
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
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The problem of combining biometric matchers for person verification can be viewed as a pattern classification problem, and any trainable pattern classification algorithm can be used for score combination. But biometric matchers of different modalities possess a property of the statistical independence of their output scores. In this work we investigate if utilizing this independence knowledge results in the improvement of the combination algorithm. We show both theoretically and experimentally that utilizing independence provides better approximation of score density functions, and results in combination improvement.