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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1069

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contributor.authorMehrotra, H-
contributor.authorSrinivas, B G-
contributor.authorMajhi, B-
date.accessioned2009-11-19T03:24:44Z-
date.available2009-11-19T03:24:44Z-
date.issued2009-
identifier.citationCommunications in Computer and Information Science, Vol 40, 2009, pages 194-204en
identifier.urihttp://dx.doi.org/10.1007/978-3-642-03547-0_19-
identifier.urihttp://hdl.handle.net/2080/1069-
description.abstractThe key concern of indexing is to retrieve small portion of database for searching the query. In the proposed paper iris database is indexed using energy histogram. The normalised iris image is divided into subbands using multiresolution DCT transformation. Energy based histogram is formed for each subband using all the images in the database. Each histogram is divided into fixed size bins to group the iris images having similar energy value. The bin number for each subband is obtained and all subands are traversed in Morton order to form a global key for each image. During database preparation the key is used to traverse the B tree. The images with same key are stored in the same leaf node. For a given query image, the key is generated and tree is traversed to end up to a leaf node. The templates stored at the leaf node are retrieved and compared with the query template to find the best match. The proposed indexing scheme is showing considerably low penetration rate of 0.63%,0.06% and 0.20% for CASIA, BATH and IITK iris databases respectively.en
format.extent382615 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherSpringeren
subjectIndexing,en
subjectEnergy Histogram,en
subjectDCT,en
subjectMultiresolution Subband Coding,en
subjectB Tree,en
subjectIris.en
titleIndexing Iris Biometric Database Using Energy Histogram of DCT Subbandsen
typeArticleen
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