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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Decision-level fusion strategies for correlated biometric classifiers
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Kalyan Veeramachaneni, Department of Electrical Engineering and Computer Science, Syracuse University, USA
Lisa Osadciw, Department of Electrical Engineering and Computer Science, Syracuse University, USA
Arun Ross, Department of Computer Science and Electrical Engineering, West Virginia University, USA
Nisha Srinivas, Department of Electrical Engineering and Computer Science, Syracuse University, USA
The focus of this paper is on designing decision-level fusion strategies for correlated biometric classifiers. In this regard, two different strategies are investigated. In the first strategy, an optimal fusion rule based on the likelihood ratio test (LRT) and the Chair Varshney Rule (CVR) is discussed for correlated hypothesis testing where the thresholds of the individual biometric classifiers are first fixed. In the second strategy, a particle swarm optimization (PSO) based procedure is proposed to simultaneously optimize the thresholds and the fusion rule. Results are presented on (a) a synthetic score data conforming to a multivariate normal distribution with different covariance matrices, and (b) the NIST BSSR dataset. We observe that the PSO-based decision fusion strategy performs well on correlated classifiers when compared with the LRT-based method as well as the average sum rule employing z-score normalization. This work highlights the importance of incorporating the correlation structure between classifiers when designing a biometric fusion system.
Citation:
Kalyan Veeramachaneni, Lisa Osadciw, Arun Ross, Nisha Srinivas, "Decision-level fusion strategies for correlated biometric classifiers," cvprw, pp.1-6, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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