Fingerprint recognition by using convoloutional neurla network and support vector machine classification
| dc.contributor.author | Al-Saedi, Ali Abdulhasan Johni | |
| dc.contributor.author | Ibrahim, Abdullahi Abdu | |
| dc.date.accessioned | 2021-05-15T12:49:31Z | |
| dc.date.available | 2021-05-15T12:49:31Z | |
| dc.date.issued | 2020 | |
| dc.department | Mühendislik ve Doğa Bilimleri Fakültesi, Bilişim Teknolojileri Bölümü | en_US |
| dc.description | 4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025 | |
| dc.description.abstract | Biometrics seeks to solve the problems of traditional verification methods by using certain physiological properties associated with an individual. Among all the biometric indicators, fingerprints have been shown to have good levels of reliability. The most widely used local representation is based on the details (minutiae) of the fingerprints. The pattern of the minutiae on a fingerprint forms a valid representation of the fingerprint. The minutiae that are most used for automatic recognition are branches and endings. However, given fingerprint acquisition techniques, it is common for endings and bifurcations to undergo deformations, which is why they are commonly referred to as minutiae. That is why in this document we will simply refer to these characteristics as minutiae. In this work we describe the results obtained using a methodology proposed for the recognition of minutiae using convolutional neural networks CNN, trained with different databases that contain fingerprints then we use the support vector machine classification to classify newly input images of fingerprints based on the features extracted by the CNN and matched with the dataset, our method proves to have better accuracy and lower MSE than the previous linear methods use for fingerprint recognition. © 2020 IEEE. | en_US |
| dc.identifier.doi | 10.1109/ISMSIT50672.2020.9255130 | |
| dc.identifier.isbn | 9781728190907 | |
| dc.identifier.scopus | 2-s2.0-85097684721 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.uri | https://doi.org/10.1109/ISMSIT50672.2020.9255130 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/1041 | |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Ibrahim, Abdullahi Abdu | |
| dc.institutionauthor | Al-Saedi, Ali Abdulhasan Johni | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | 4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 - Proceedings | |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | CNN | en_US |
| dc.subject | Minutiae | en_US |
| dc.subject | MSE | en_US |
| dc.subject | Neural Network | en_US |
| dc.subject | SVM | en_US |
| dc.title | Fingerprint recognition by using convoloutional neurla network and support vector machine classification | |
| dc.type | Conference Object |
