An efficient finger-vein extraction algorithm based on random forest regression with efficient local binary patterns
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Liu, C. | - |
dc.contributor.author | Kim, Yeong-Hwa | - |
dc.date.accessioned | 2021-12-07T07:40:17Z | - |
dc.date.available | 2021-12-07T07:40:17Z | - |
dc.date.issued | 2016-09 | - |
dc.identifier.issn | 1522-4880 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/52304 | - |
dc.description.abstract | Finger-vein, as a secure and convenient biometric characteristic in nature, has been widely studied for authentication in recent years. In this paper, we propose an efficient finger-vein extraction algorithm based on random forest training and regression with efficient local binary pattern feature. By integrating with a vein pattern matching method which is robust to finger misalignment, we achieved state-of-the-art finger-vein recognition. Thorough experiments have been conducted on two popular databases to prove the effectiveness and robustness of the proposed method. © 2016 IEEE. | - |
dc.format.extent | 5 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE Computer Society | - |
dc.title | An efficient finger-vein extraction algorithm based on random forest regression with efficient local binary patterns | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICIP.2016.7532938 | - |
dc.identifier.bibliographicCitation | Proceedings - International Conference on Image Processing, ICIP, v.2016-August, pp 3141 - 3145 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000390782003032 | - |
dc.identifier.scopusid | 2-s2.0-85006820962 | - |
dc.citation.endPage | 3145 | - |
dc.citation.startPage | 3141 | - |
dc.citation.title | Proceedings - International Conference on Image Processing, ICIP | - |
dc.citation.volume | 2016-August | - |
dc.type.docType | Conference Paper | - |
dc.subject.keywordAuthor | Finger-vein Extraction | - |
dc.subject.keywordAuthor | Finger-vein Recognition | - |
dc.subject.keywordAuthor | Local Binary Pattern | - |
dc.subject.keywordAuthor | Random Forest Regression | - |
dc.subject.keywordPlus | Bins | - |
dc.subject.keywordPlus | Content based retrieval | - |
dc.subject.keywordPlus | Decision trees | - |
dc.subject.keywordPlus | Extraction | - |
dc.subject.keywordPlus | Image matching | - |
dc.subject.keywordPlus | Palmprint recognition | - |
dc.subject.keywordPlus | Pattern matching | - |
dc.subject.keywordPlus | Regression analysis | - |
dc.subject.keywordPlus | Finger vein | - |
dc.subject.keywordPlus | Finger-vein recognition | - |
dc.subject.keywordPlus | Local binary patterns | - |
dc.subject.keywordPlus | Random forests | - |
dc.subject.keywordPlus | State of the art | - |
dc.subject.keywordPlus | Vein pattern | - |
dc.subject.keywordPlus | Image processing | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.description.journalRegisteredClass | scopus | - |
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