Exploring gender prediction from iris biometrics

DA COSTA ABREU, Marjory, FAIRHURST, M. and ERBILEK, M. (2015). Exploring gender prediction from iris biometrics. In: 2015 International Conference of the Biometrics Special Interest Group (BIOSIG). IEEE.

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Official URL: https://ieeexplore.ieee.org/document/7314602
Link to published version:: https://doi.org/10.1109/BIOSIG.2015.7314602
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    Abstract

    Prediction of gender characteristics from iris images has been investigated and some successful results have been reported in the literature, but without considering performance for different iris features and classifiers. This paper investigates for the first time an approach to gender prediction from iris images using different types of features (including a small number of very simple geometric features, texture features and a combination of geometric and texture features) and a more versatile and intelligent classifier structure. Our proposed approaches can achieve gender prediction accuracies of up to 90% in the BioSecure Database.

    Item Type: Book Section
    Additional Information: ISSN: 1617-5468
    Identification Number: https://doi.org/10.1109/BIOSIG.2015.7314602
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 25 Nov 2021 15:51
    Last Modified: 26 Nov 2021 11:15
    URI: http://shura.shu.ac.uk/id/eprint/27292

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