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Chen, Chun and Veldhuis, R.N.J.
(2008)
Performances of the likelihood-ratio classifier based on different data modelings.
In: 10th International Conference on Control, Automation, Robotics and Vision, 2008. ICARCV 2008., 17-20 Dec 2008, Hanoi, Vietnam.
pp. 1347-1351.
IEEE Computer Society.
ISBN 978-1-4244-2287-6
Full text available as:
Official URL: http://dx.doi.org/10.1109/ICARCV.2008.4795718  AbstractThe 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.
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