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Performances of the likelihood-ratio classifier based on different data modelings

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2 Author(s)
C. Chen ; Signals and Systems Group, Electrical Engineeing, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands ; R. N. J. Veldhuis

The classical likelihood ratio classifier easily collapses in many biometric applications especially with independent training-test subjects. The reason lies in the inaccurate estimation of the underlying user-specific feature density. Firstly, the feature density estimation suffers from insufficient number of user-specific samples during the enrollment phase. Even if more enrollment samples are available, it is most likely that they are not reliable enough. Furthermore, it may happen that enrolled samples do not obey the Gaussian density model. Therefore, it is crucial to properly estimate the underlying user-specific feature density in the above situations. In this paper, we give an overview of several data modeling methods. Furthermore, we propose a discretized density based data model. Experimental results on FRGC face data set has shown reasonably good performance with our proposed model.

Published in:

Control, Automation, Robotics and Vision, 2008. ICARCV 2008. 10th International Conference on

Date of Conference:

17-20 Dec. 2008