Automatic Eye Detection Error as a Predictor of Face Recognition Performance

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    12 Citations (Scopus)
    14 Downloads (Pure)

    Abstract

    Various facial image quality parameters like pose, illumination, noise, resolution, etc are known to be a predictor of face recognition performance. However, there still remain many other properties of facial images that are not captured by the existing quality parameters. In this paper, we propose a novel image quality parameter called the Automatic Eye Detection Error (AEDE) which measures the difference between manually located and automatically detected eye coordinates. Our experiment results carried out using FaceVACS recognition system and the MultiPIE dataset show that AEDE is indeed a predictor of face recognition performance.
    Original languageEnglish
    Title of host publication35rd WIC Symposium on Information Theory in the Benelux and The 4th WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux
    Subtitle of host publicationEindhoven, The Netherlands, May 12-13, 2014
    Place of PublicationEindhoven
    PublisherWerkgemeenschap voor Informatie- en Communicatietheorie (WIC)
    Pages89-96
    Number of pages8
    ISBN (Print)978-90-365-3383-6
    Publication statusPublished - May 2014
    Event35th WIC Symposium on Information Theory in the Benelux 2014 - Eindhoven, Netherlands
    Duration: 12 May 201413 May 2014
    Conference number: 35

    Conference

    Conference35th WIC Symposium on Information Theory in the Benelux 2014
    CountryNetherlands
    CityEindhoven
    Period12/05/1413/05/14

    Keywords

    • SCS-Safety
    • Performance Prediction
    • EWI-24813
    • IR-91485
    • Face Recognition
    • METIS-305908
    • Eye Detection Error

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