Periocular biometric recognition using image sets

M. Uzair, Arif Mahmood, Ajmal Mian, Chris Mcdonald

    Research output: Chapter in Book/Conference paperConference paper

    17 Citations (Scopus)
    297 Downloads (Pure)

    Abstract

    Human identification based on iris biometrics requires high resolution iris images of a cooperative subject. Such images cannot be obtained in non-intrusive applications such as surveillance. However, the full region around the eye, known as the periocular region, can be acquired non-intrusively and used as a biometric. In this paper we investigate the use of periocular region for person identification. Current techniques have focused on choosing a single best frame, mostly manually, for matching. In contrast, we formulate, for the first time, person identification based on periocular regions as an image set classification problem. We generate periocular region image sets from the Multi Bio-metric Grand Challenge (MBGC) NIR videos. Periocular regions of the right eyes are mirrored and combined with those of the left eyes to form an image set. Each image set contains periocular regions of a single subject. For imageset classification, we use six state-of-the-art techniques and report their comparative recognition and verification performances. Our results show that image sets of periocular regions achieve significantly higher recognition rates than currently reported in the literature for the same database.
    Original languageEnglish
    Title of host publication2013 IEEE Workshop on Applications of Computer Vision (WACV)
    Place of PublicationUnited States
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages246-251
    ISBN (Print)9781467350532
    DOIs
    Publication statusPublished - Jan 2013
    Event2013 IEEE Workshop on Applications of Computer Vision - Tampa, United States
    Duration: 15 Jan 201317 Jan 2013

    Conference

    Conference2013 IEEE Workshop on Applications of Computer Vision
    Abbreviated titleWACV 2013
    CountryUnited States
    CityTampa
    Period15/01/1317/01/13

    Fingerprint

    Image recognition
    Biometrics
    Image resolution

    Cite this

    Uzair, M., Mahmood, A., Mian, A., & Mcdonald, C. (2013). Periocular biometric recognition using image sets. In 2013 IEEE Workshop on Applications of Computer Vision (WACV) (pp. 246-251). United States: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/WACV.2013.6475025
    Uzair, M. ; Mahmood, Arif ; Mian, Ajmal ; Mcdonald, Chris. / Periocular biometric recognition using image sets. 2013 IEEE Workshop on Applications of Computer Vision (WACV). United States : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 246-251
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    title = "Periocular biometric recognition using image sets",
    abstract = "Human identification based on iris biometrics requires high resolution iris images of a cooperative subject. Such images cannot be obtained in non-intrusive applications such as surveillance. However, the full region around the eye, known as the periocular region, can be acquired non-intrusively and used as a biometric. In this paper we investigate the use of periocular region for person identification. Current techniques have focused on choosing a single best frame, mostly manually, for matching. In contrast, we formulate, for the first time, person identification based on periocular regions as an image set classification problem. We generate periocular region image sets from the Multi Bio-metric Grand Challenge (MBGC) NIR videos. Periocular regions of the right eyes are mirrored and combined with those of the left eyes to form an image set. Each image set contains periocular regions of a single subject. For imageset classification, we use six state-of-the-art techniques and report their comparative recognition and verification performances. Our results show that image sets of periocular regions achieve significantly higher recognition rates than currently reported in the literature for the same database.",
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    Uzair, M, Mahmood, A, Mian, A & Mcdonald, C 2013, Periocular biometric recognition using image sets. in 2013 IEEE Workshop on Applications of Computer Vision (WACV). IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 246-251, 2013 IEEE Workshop on Applications of Computer Vision , Tampa, United States, 15/01/13. https://doi.org/10.1109/WACV.2013.6475025

    Periocular biometric recognition using image sets. / Uzair, M.; Mahmood, Arif; Mian, Ajmal; Mcdonald, Chris.

    2013 IEEE Workshop on Applications of Computer Vision (WACV). United States : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 246-251.

    Research output: Chapter in Book/Conference paperConference paper

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    N2 - Human identification based on iris biometrics requires high resolution iris images of a cooperative subject. Such images cannot be obtained in non-intrusive applications such as surveillance. However, the full region around the eye, known as the periocular region, can be acquired non-intrusively and used as a biometric. In this paper we investigate the use of periocular region for person identification. Current techniques have focused on choosing a single best frame, mostly manually, for matching. In contrast, we formulate, for the first time, person identification based on periocular regions as an image set classification problem. We generate periocular region image sets from the Multi Bio-metric Grand Challenge (MBGC) NIR videos. Periocular regions of the right eyes are mirrored and combined with those of the left eyes to form an image set. Each image set contains periocular regions of a single subject. For imageset classification, we use six state-of-the-art techniques and report their comparative recognition and verification performances. Our results show that image sets of periocular regions achieve significantly higher recognition rates than currently reported in the literature for the same database.

    AB - Human identification based on iris biometrics requires high resolution iris images of a cooperative subject. Such images cannot be obtained in non-intrusive applications such as surveillance. However, the full region around the eye, known as the periocular region, can be acquired non-intrusively and used as a biometric. In this paper we investigate the use of periocular region for person identification. Current techniques have focused on choosing a single best frame, mostly manually, for matching. In contrast, we formulate, for the first time, person identification based on periocular regions as an image set classification problem. We generate periocular region image sets from the Multi Bio-metric Grand Challenge (MBGC) NIR videos. Periocular regions of the right eyes are mirrored and combined with those of the left eyes to form an image set. Each image set contains periocular regions of a single subject. For imageset classification, we use six state-of-the-art techniques and report their comparative recognition and verification performances. Our results show that image sets of periocular regions achieve significantly higher recognition rates than currently reported in the literature for the same database.

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    Uzair M, Mahmood A, Mian A, Mcdonald C. Periocular biometric recognition using image sets. In 2013 IEEE Workshop on Applications of Computer Vision (WACV). United States: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 246-251 https://doi.org/10.1109/WACV.2013.6475025