Superpixel-based Zernike moments for palm-print recognition
| dc.contributor.author | Bilal, Attallah | |
| dc.date.accessioned | 2019-11-14T13:38:30Z | |
| dc.date.available | 2019-11-14T13:38:30Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | In the contemporary period, significant attention has been focused on the prospects of innovative personal recognition methods based on palm-print biometrics. However, diminished local consistency and interference from noise are only some of the obstacles that hinder the most common methods of palm-print imaging such as the grey texture and other low-level of the palm. Nevertheless, the development of the process and tackling of the obstacles faced have a potential solution in the form of high-level characteristic imaging for palm-print identification. In this study, Zernike moments are used for acquiring superpixel features that are spiral scanned images, which is an innovative recognition method. By using the extreme learning machine, the inter- and intra-similarities of the palm-print feature maps are determined. Our experiments yield good results with an accuracy rate of 97.52 and an equal error rate of 1.47% on the palm-print PolyU database. | en_US |
| dc.identifier.uri | https://repository.univ-msila.dz/handle/123456789/18601 | |
| dc.publisher | Université de M'sila | en_US |
| dc.subject | palm-print recognition; image segmentation; feature extraction; extreme learning machine; ELM; image matching. | en_US |
| dc.title | Superpixel-based Zernike moments for palm-print recognition | en_US |
| dc.type | Article | en_US |