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dc.contributor.authorAl-Waisy, Alaa S.*
dc.contributor.authorQahwaji, Rami S.R.*
dc.contributor.authorIpson, Stanley S.*
dc.contributor.authorAl-Fahdawi, Shumoos*
dc.date.accessioned2018-10-11T10:33:57Z
dc.date.available2018-10-11T10:33:57Z
dc.date.issued2015
dc.identifier.citationAl-Waisy AS, Qahwaji R, Ipson S and Al-Fahdawi S (2015) A Fast and Accurate Iris Localization Technique for Healthcare Security System. 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, 26-28 Oct. IEEE. pp 1028-1034.en_US
dc.identifier.urihttp://hdl.handle.net/10454/16599
dc.descriptionyesen_US
dc.description.abstractIn the health care systems, a high security level is required to protect extremely sensitive patient records. The goal is to provide a secure access to the right records at the right time with high patient privacy. As the most accurate biometric system, the iris recognition can play a significant role in healthcare applications for accurate patient identification. In this paper, the corner stone towards building a fast and robust iris recognition system for healthcare applications is addressed, which is known as iris localization. Iris localization is an essential step for efficient iris recognition systems. The presence of extraneous features such as eyelashes, eyelids, pupil and reflection spots make the correct iris localization challenging. In this paper, an efficient and automatic method is presented for the inner and outer iris boundary localization. The inner pupil boundary is detected after eliminating specular reflections using a combination of thresholding and morphological operations. Then, the outer iris boundary is detected using the modified Circular Hough transform. An efficient preprocessing procedure is proposed to enhance the iris boundary by applying 2D Gaussian filter and Histogram equalization processes. In addition, the pupil’s parameters (e.g. radius and center coordinates) are employed to reduce the search time of the Hough transform by discarding the unnecessary edge points within the iris region. Finally, a robust and fast eyelids detection algorithm is developed which employs an anisotropic diffusion filter with Radon transform to fit the upper and lower eyelids boundaries. The performance of the proposed method is tested on two databases: CASIA Version 1.0 and SDUMLA-HMT iris database. The Experimental results demonstrate the efficiency of the proposed method. Moreover, a comparative study with other established methods is also carried out.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.156en_US
dc.rights© 2015 IEEE. Reproduced in accordance with the publisher's self-archiving policy. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.subjectIris localizationen_US
dc.subjectIris segmentationen_US
dc.subjectRadon transformen_US
dc.subjectCircular Hough transformen_US
dc.subjectSDUMLA-HMT iris databaseen_US
dc.subjectCASIA databaseen_US
dc.subjectIris recognitionen_US
dc.subjectHealthcare security systemen_US
dc.subjectPatient identificationen_US
dc.titleA Fast and Accurate Iris Localization Technique for Healthcare Security Systemen_US
dc.status.refereedn/aen_US
dc.typeConference paperen_US
dc.type.versionAccepted Manuscripten_US
refterms.dateFOA2018-10-11T10:33:57Z


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