Human identification with finger vien image using deep learning
| dc.contributor.author | Jasim, Zinah Khalid Jasim | |
| dc.contributor.author | Mohammed, Alaa Hamid | |
| dc.contributor.author | Elwiya, Lina | |
| dc.contributor.author | Al-Jabbari, Baraa Dhafer | |
| dc.contributor.author | Alhaji, Haitham | |
| dc.date.accessioned | 2022-02-04T12:17:50Z | |
| dc.date.available | 2022-02-04T12:17:50Z | |
| dc.date.issued | 2021 | en_US |
| dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
| dc.description.abstract | The technical term for body measurements and estimates is called biometrics. It refers to metrics related to human characteristics. Biometrics classification is using in computer science as a form of identification and access control. Classification is one of the pattern recognition methods that consist of grouping similar data into classes. Automated personal identification using vascular biometrics. The Convolutional Neural Network (CNN) has demonstrated its remarkable ability to learn biometric traits that can provide a robust and accurate match. This thesis aims to develop a robust finger-vein identification system using CNN. Since finger vein lies under the human body, so they need Near Infrared (NIR) light and camera for acquiring, the finger-vein require spectrum light with the camera to capture. The capture images need to pass through several stages, including reprocessing, pattern extraction, and matching, to decide to get an individual ID. This research proposes an efficient deep learning model to build a robust finger vein identification system. After images preprocessing and vein pattern extraction, feature extraction and matching are performed by the proposed CNN model, which has one input layer and more than one hidden layer and one output layer. The first hidden layer is known as the convolution layer its plays the role of feature extraction and produces features map, followed by the pooling layer, which acts as a filter to remove unwilling features, and batch Normalization layer to speed up the training process. The system presented 99.78 % accuracy which is remarkable when compared with several researches. | en_US |
| dc.identifier.citation | Jasim, Z. K. J., Mohammed, A. H., Elwiya, L., Al-Jabbari, B. D., & Alhaji, H. (2021, October). Human Identification with Finger Vien Image Using Deep Learning. In 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 672-677). IEEE. | en_US |
| dc.identifier.endpage | 677 | en_US |
| dc.identifier.scopus | 2-s2.0-85123303804 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.startpage | 672 | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/2243 | |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Jasim, Zinah Khalid Jasim | |
| dc.institutionauthor | Elwiya, Lina | |
| dc.institutionauthor | Al-Jabbari, Baraa Dhafer | |
| dc.institutionauthor | Alhaji, Haitham | |
| dc.language.iso | en | |
| dc.publisher | IEEE | en_US |
| dc.relation.ispartof | IEEE | |
| dc.relation.isversionof | 10.1109/ISMSIT52890.2021.9604672 | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | CNN | en_US |
| dc.subject | KNN | en_US |
| dc.subject | SVM | en_US |
| dc.subject | NN | en_US |
| dc.subject | Vein Recognition | en_US |
| dc.title | Human identification with finger vien image using deep learning | |
| dc.type | Article |
