Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/55211
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dc.contributor.authorSingh, Harivans Pratap-
dc.contributor.authorDimri, Priti-
dc.contributor.authorTiwari, Shailesh-
dc.contributor.authorSaraswat, Manish-
dc.date.accessioned2020-09-23T06:10:20Z-
dc.date.available2020-09-23T06:10:20Z-
dc.date.issued2020-08-
dc.identifier.issn0975-1084 (Online); 0022-4456 (Print)-
dc.identifier.urihttp://nopr.niscair.res.in/handle/123456789/55211-
dc.description730-753en_US
dc.description.abstractSince decades fingerprints have been the prime source in identification of suspects latent fingerprints are compared and examined with rolled and plain fingerprints which are stored in the dataset. The common challenges which are faced while examining latent fingerprints are background noise, nonlinear distortions, poor ridge clarity and partial impression of the finger. As conventional methods of Segmentation doesn’t perform well on latent fingerprints. The current advancement in machine learning based segmentation approach has been showing good results in terms of segmentation accuracy but lacks to provide accurate result in terms of matching accuracy. As one of the problem faced in matching latent fingerprint is low clarity of ridge-valley pattern which results in detection of false minutiae and poor matching accuracy. A multilayer processing of artificial neural network based segmentation is proposed to minimize the detection of false minutiae and increase the matching accuracy. This approach is designed on binary classification model where the simulation will be carried out on IIIT-D latent fingerprint dataset. Segmentation will be divided into full and partial impression fingerprints which are then compared with minutiae with the database using local and global matching algorithm. An improvised result is received which is more accurate as compared to the previous algorithms.en_US
dc.language.isoen_USen_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceJSIR Vol.79(08) [August 2020]en_US
dc.subjectLatent fingerprintsen_US
dc.subjectMinutiaeen_US
dc.subjectMulti-layer Neural networken_US
dc.subjectSegmentationen_US
dc.titleLatent Fingerprint Indexing for Faster Retrieval from Dataset with Image Enhancement Techniqueen_US
dc.typeArticleen_US
Appears in Collections:JSIR Vol.79(08) [August 2020]

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