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dc.contributor.authorDas, Abhijit
dc.contributor.authorPal, Umapada
dc.contributor.authorFerrer Ballester, Miguel Angel
dc.contributor.authorBlumenstein, Michael
dc.contributor.editorHaibo He
dc.date.accessioned2017-05-03T16:15:20Z
dc.date.available2017-05-03T16:15:20Z
dc.date.issued2014
dc.identifier.refurihttp://ieee-ssci.org/CIBIM.html
dc.identifier.doi10.1109/CIBIM.2014.7015439
dc.identifier.urihttp://hdl.handle.net/10072/68401
dc.description.abstractThis piece of work proposes a liveliness based sclera eye biometric, validation and recognition technique at a distance. The images in this work are acquired by a digital camera in the visible spectrum at varying distance of about 1 meter from the individual. Each individual during registration as well as validation is asked to look straight and move their eye ball up, left and right keeping their face straight to incorporate liveliness of the data. At first the image is divided vertically into two halves and the eyes are detected in each half of the face image that is captured, by locating the eye ball by a Circular Hough Transform. Then the eye image is cropped out automatically using the radius of the iris. Next a C-means-based segmentation is used for sclera segmentation followed by vessel enhancement by the adaptive histogram equalization and Haar filtering. The feature extraction was performed by patch-based Dense-LDP (Linear Directive Pattern). Furthermore each training image is used to form a bag of features, which is used to produce the training model. Each of the images of the different poses is combined at the feature level and the image level to obtain higher accuracy and to incorporate liveliness. The fusion that produces the best result is considered. Support Vector Machines (SVMs) are used for classification. Here images from 82 individuals (both left and right eye i.e. 164 different eyes) are used and an appreciable Equal Error Rate of 0.52% is achieved in this work.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.publisherIEEE
dc.publisher.placeUnited States
dc.publisher.urihttp://ieee-ssci.org/CIBIM.html
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofconferencenameCIBIM 2014
dc.relation.ispartofconferencetitleComputational Intelligence in Biometrics and Identity Management (CIBIM), 2014 IEEE Symposium on
dc.relation.ispartofdatefrom2014-12-09
dc.relation.ispartofdateto2014-12-12
dc.relation.ispartoflocationOrlando, Florida, United States
dc.rights.retentionY
dc.subject.fieldofresearchArtificial intelligence not elsewhere classified
dc.subject.fieldofresearchcode460299
dc.titleMulti-angle Based Lively Sclera Biometrics at a Distance
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorBlumenstein, Michael M.
gro.griffith.authorDas, Abhijit


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    Contains papers delivered by Griffith authors at national and international conferences.

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