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

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    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.
    Original languageUndefined
    Title of host publication10th International Conference on Control, Automation, Robotics and Vision, 2008. ICARCV 2008.
    Place of PublicationLos Alamitos
    PublisherIEEE Computer Society Press
    Number of pages5
    ISBN (Print)978-1-4244-2287-6
    Publication statusPublished - 17 Dec 2008
    Event10th International Conference on Control, Automation, Robotics & Vision, ICARCV 2008 - Melia Hanoi, Hanoi, Viet Nam
    Duration: 17 Dec 200820 Dec 2008
    Conference number: 10

    Publication series

    PublisherIEEE Computer Society Press


    Conference10th International Conference on Control, Automation, Robotics & Vision, ICARCV 2008
    Abbreviated titleICARCV
    Country/TerritoryViet Nam


    • SCS-Safety
    • EWI-15180
    • CR-I.5
    • METIS-256467
    • likelihood-ratio classifier
    • IR-65417
    • Density estimation
    • Quantization

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