Latent fingerprint enhancement and segmentation technique based on hybrid edge adaptive DTV model

dc.authorscopusid 56366094100
dc.authorscopusid 56405641800
dc.authorscopusid 56642726000
dc.authorscopusid 57258281900
dc.authorscopusid 57195625873
dc.authorscopusid 57258623000
dc.authorscopusid 57258623000
dc.contributor.author Hilles,S.M.S.
dc.contributor.author Liban,A.
dc.contributor.author Miaikil,O.A.M.
dc.contributor.author Mahmoud Altrad,A.
dc.contributor.author Baker El-Ebiary,Y.A.
dc.contributor.author Hilles,M.M.
dc.contributor.author Contreras,J.
dc.date.accessioned 2024-05-25T12:34:14Z
dc.date.available 2024-05-25T12:34:14Z
dc.date.issued 2021
dc.department Okan University en_US
dc.department-temp Hilles S.M.S., Istanbul OKAN University, Software Engineering Department, Istanbul, Turkey; Liban A., Hargeisa University, Computer Science Department, Hargeisa, Somalia; Miaikil O.A.M., Al-Madinah International University, Computer Science Department, Kuala Lumpur, Malaysia; Mahmoud Altrad A., Al-Madinah International University, Computer Science Department, Kuala Lumpur, Malaysia; Baker El-Ebiary Y.A., Universiti Sultan Zainal Abidin, Faculty of Informatics and Computing, Trengganu, Malaysia; Hilles M.M., University College of Applied Science, Information Technology Department, Gaza, Palestine; Contreras J., FEU Institute of Technology, Department of Information Technology, Manila, Philippines en_US
dc.description.abstract Image enhancement and segmentation is widely used for fingerprint identification and authorization in biometrics devices, criminal scene is most challenges due to low quality of fingerprint, the most significant efforts is to develop algorithm for latent fingerprint enhancement which become challenging problem due to the complex and existing problem for instance, developing algorithms of latent fingerprint is able to extract features of image blocks and removing overlapping and isolate the poor and noisy background. however, it's still challenging and interested problem specifically latent fingerprint enhancement and segmentation. The aim study of this paper is to propose latent fingerprint enhancement and segmentation based on hybrid model and Chan-Vese method for segmentation, in order to reduce low image quality and increase the accuracy of fingerprint. The desired characteristics of intended technique are adaptive, effective and accurate, hybrid model of edge adaptive direction achieves accurate latent fingerprint enhancement and segmentation, the target needs to improve feature detection and performance, this research has proposed system architecture of research method in fingerprint enhancement and segmentation where is the method content two stages, the first is normalization and second is reconstruction, using EDTV model is required for adaptive noise, in addition Chan-vase technique contributed for identification of fingerprint image features, the result and testing using RMSE with three categories of fingerprint images good, bad and ugly show better performance for all three categories, as well RMSE shows the average of good latent fingerprint before and after enhancement. Latent Fingerprint Enhancement and Segmentation Technique Based on Hybrid Model Edge Adaptive Directional Total Variation © 2021 IEEE. en_US
dc.identifier.citationcount 14
dc.identifier.doi 10.1109/ICSCEE50312.2021.9498025
dc.identifier.endpage 13 en_US
dc.identifier.isbn 978-166543222-1
dc.identifier.scopus 2-s2.0-85114852372
dc.identifier.startpage 8 en_US
dc.identifier.uri https://doi.org/10.1109/ICSCEE50312.2021.9498025
dc.identifier.uri https://hdl.handle.net/20.500.14517/2562
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof 2021 2nd International Conference on Smart Computing and Electronic Enterprise: Ubiquitous, Adaptive, and Sustainable Computing Solutions for New Normal, ICSCEE 2021 -- 2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021 -- 15 June 2021 through 16 June 2021 -- Virtual, Online -- 171212 en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 14
dc.subject Enhancement en_US
dc.subject Hybrid model en_US
dc.subject Image en_US
dc.subject Segmentation en_US
dc.title Latent fingerprint enhancement and segmentation technique based on hybrid edge adaptive DTV model en_US
dc.type Conference Object en_US

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