Abstract
Statistical dependence of classifiers has recently been shown to improve accuracy over statistically independent classifiers. In this paper, we focus on the verification application and theoretically analyze the AND fusion rule to find the favorable conditional dependence that improves the fusion accuracy over conditionally independent classifiers. Based on this analysis, we come with a method to design such classifiers by training the classifiers on different partitions of the training data. The AR face database is used for performance evaluation and the proposed method has a false rejection rate (FRR) of 2.4% and a false acceptance rate of 3.3% on AND fusion, which is better than an FRR of 3.8% and FAR of 4.3% when classifiers are designed without taking account the AND fusion rule.
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Venkataramani, K., Kumar, B.V.K.V. (2005). Conditionally Dependent Classifier Fusion Using AND Rule for Improved Biometric Verification. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_31
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DOI: https://doi.org/10.1007/11552499_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
Online ISBN: 978-3-540-31999-3
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