Palmprint recognition system based on deep region of interest features with the aid of hybrid approach
| dc.contributor.author | Türk, Ömer | |
| dc.contributor.author | Çalışkan, Abidin | |
| dc.contributor.author | Acar, Emrullah | |
| dc.contributor.author | Ergen, Burhan | |
| dc.date.accessioned | 2023-08-01T10:48:12Z | |
| dc.date.available | 2023-08-01T10:48:12Z | |
| dc.date.issued | 2023 | en_US |
| dc.department | MAÜ, Meslek Yüksekokulları, Mardin Meslek Yüksekokulu, Bilgisayar Teknolojileri Bölümü | en_US |
| dc.description.abstract | Palmprint recognition system is a biometric technology, which is promising to have a high precision. This system has started to attract the attention of researchers, especially with the emergence of deep learning techniques in recent years. In this study, a deep learning and machine learning-based hybrid approach has been recommended to recognize palmprint images automatically via region of interest (ROI) features. The proposed work consists of several stages, respectively. In the first stage, the raw images have been collected from the PolyU database and preprocessing operations have been implemented in order to determine ROI areas. In the second stage, deep ROI features have been extracted from the preprocessed images with the aid of deep learning technique. In the last stage, the obtained deep features have been classified by employing a hybrid deep convolutional neural network and support vector machine models. Finally, it has been observed that the overall accuracy of the proposed system has achieved very successful results as 99.72% via hybrid approach. Moreover, very low execution time has been observed for whole process of the proposed system with 0.10 s. | en_US |
| dc.identifier.citation | Türk, Ö., Çalışkan, A., Acar, E., & Ergen, B. (2023). Palmprint recognition system based on deep region of interest features with the aid of hybrid approach. Signal, Image and Video Processing, 1-9. | en_US |
| dc.identifier.doi | 10.1007/s11760-023-02612-0 | en_US |
| dc.identifier.scopus | 2-s2.0-85160238017 | en_US |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.uri | https://doi.org/10.1007/s11760-023-02612-0 | |
| dc.identifier.uri | https://www.scopus.com/record/display.uri?eid=2-s2.0-85160238017&origin=SingleRecordEmailAlert&dgcid=raven_sc_affil_en_us_email&txGid=95b152c55fb24fc47e6cac9e8b486492 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12514/3572 | |
| dc.identifier.wos | WOS:000994090900001 | en_US |
| dc.identifier.wosquality | N/A | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | SpringerLink | en_US |
| dc.relation.ispartof | Signal, Image and Video Processing | 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 | Palmprint · ROI · Deep learning · CNN · SVM | en_US |
| dc.title | Palmprint recognition system based on deep region of interest features with the aid of hybrid approach | en_US |
| dc.type | Article | en_US |
