Abstract
One of the traditional criteria to estimate the value of coefficients of multivariate polynomials in regression applications is MSE, which is known as OWM in classifier combination literature. In this paper, we address the use of GradientBoost algorithm to estimate coefficients of multivariate polynomials for score fusion level in multimodal biometric systems. Our experiments on NIST-bssr1 score database showed an improvement in verification accuracy and also reduction of number of coefficients, which increased the memory efficiency. In addition, we examined combination of OWM, and GradientBoost which showed better ROC performance and lower model order compared to OWM alone.
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Parviz, M., Moin, M.S. (2008). Multivariate Polynomials Estimation Based on GradientBoost in Multimodal Biometrics. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2008. Communications in Computer and Information Science, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85930-7_60
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DOI: https://doi.org/10.1007/978-3-540-85930-7_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85929-1
Online ISBN: 978-3-540-85930-7
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