Rotation Tolerant Finger Vein Recognition using CNNs
dc.contributor.author | Promegger, Bernhard | |
dc.contributor.author | Wimmer, Georg | |
dc.contributor.author | Uhl, Andreas | |
dc.contributor.editor | Brömme, Arslan | |
dc.contributor.editor | Busch, Christoph | |
dc.contributor.editor | Damer, Naser | |
dc.contributor.editor | Dantcheva, Antitza | |
dc.contributor.editor | Gomez-Barrero, Marta | |
dc.contributor.editor | Raja, Kiran | |
dc.contributor.editor | Rathgeb, Christian | |
dc.contributor.editor | Sequeira, Ana | |
dc.contributor.editor | Uhl, Andreas | |
dc.date.accessioned | 2021-10-04T08:43:50Z | |
dc.date.available | 2021-10-04T08:43:50Z | |
dc.date.issued | 2021 | |
dc.description.abstract | Finger vein recognition deals with the recognition of subjects based on their venous pattern within the fingers. The majority of the available systems acquire the vein pattern using only a single camera. Such systems are susceptible to misplacements of the finger during acquisition, in particular longitudinal finger rotation poses a severe problem. Besides some hardware based approaches that try to avoid the misplacement in the first place, there are several software based solutions to counter fight longitudinal finger rotation. All of them use classical hand-crafted features. This work presents a novel approach to make CNNs robust to longitudinal finger rotation by training CNNs using finger vein images from varying perspectives. | en |
dc.identifier.isbn | 978-3-88579-709-8 | |
dc.identifier.pissn | 1617-5468 | |
dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/37466 | |
dc.language.iso | en | |
dc.publisher | Gesellschaft für Informatik e.V. | |
dc.relation.ispartof | BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group | |
dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-328 | |
dc.subject | Finger vein recognition | |
dc.subject | longitudinal finger rotation | |
dc.subject | rotation tolerance | |
dc.subject | CNN | |
dc.title | Rotation Tolerant Finger Vein Recognition using CNNs | en |
dc.type | Text/Conference Paper | |
gi.citation.endPage | 284 | |
gi.citation.publisherPlace | Bonn | |
gi.citation.startPage | 277 | |
gi.conference.date | 15.-17. September 2021 | |
gi.conference.location | International Digital Conference | |
gi.conference.sessiontitle | Further Conference Contributions |
Dateien
Originalbündel
1 - 1 von 1
Lade...
- Name:
- biosig2021_proceedings_30.pdf
- Größe:
- 240.19 KB
- Format:
- Adobe Portable Document Format