Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
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Computer Vision and Image Understanding
Biometric authentication: a machine learning approach
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Signal Processing
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Pattern Recognition
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A new ensemble diversity measure applied to thinning ensembles
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To improve accuracy and reliability, Boolean combination (BC) can efficiently integrate the responses of multiple biometric systems in the ROC space. However, BC techniques assume that recognition systems are conditionally-independent and that their ROC curves are convex. These assumptions are rarely valid in practice, where systems face complex environments, and are designed using limited enrollment data. In recent research, the authors have introduced an Iterative BC (IBC) technique that applies all Boolean functions iteratively, without prior assumptions. In this paper, IBC is considered for fusion of different commercial biometric systems at the decision level. Performance of IBC is assessed for biometric authentication applications in which the operational response of unimodal iris-base systems are combined. Experiments performed with four different commercial systems using anonymised data collected by the Canada Border Services Agency indicate that IBC fusion with interpolation can signicantly outperform related BC techniques and individual systems.