A Bayesian model for predicting face recognition performance using image quality

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    Quality of a pair of facial images is a strong indicator of the uncertainty in decision about identity based on that image pair. In this paper, we describe a Bayesian approach to model the relation between image quality (like pose, illumination, noise, sharpness, etc) and corresponding face recognition performance. Experiment results based on the MultiPIE data set show that our model can accurately aggregate verification samples into groups for which the verification performance varies fairly consistently. Our model does not require similarity scores and can predict face recognition performance using only image quality information. Such a model has many applications. As an illustrative application, we show improved verification performance when the decision threshold automatically adapts according to the quality of facial images.
    Original languageUndefined
    Title of host publication2014 IEEE International Joint Conference on Biometrics (IJCB)
    Place of PublicationUSA
    Number of pages8
    ISBN (Print)978-1-4799-3584-0
    Publication statusPublished - 29 Sept 2014
    Event2014 IEEE International Joint Conference on Biometrics, IJCB 2014 - Clearwater, United States
    Duration: 29 Sept 20142 Oct 2014

    Publication series



    Conference2014 IEEE International Joint Conference on Biometrics, IJCB 2014
    Abbreviated titleIJCB
    Country/TerritoryUnited States


    • SCS-Cybersecurity
    • recognition performance
    • EWI-25577
    • METIS-309819
    • Face Recognition
    • IR-93651
    • Image quality

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