Full metadata record
| DC pole | Hodnota | Jazyk |
|---|---|---|
| dc.contributor.author | Lefkovits, Szidónia | |
| dc.contributor.author | Lefkovits, László | |
| dc.contributor.author | Szilágyi, László | |
| dc.contributor.editor | Skala, Václav | |
| dc.date.accessioned | 2019-10-23T06:52:51Z | |
| dc.date.available | 2019-10-23T06:52:51Z | |
| dc.date.issued | 2019 | |
| dc.identifier.citation | WSCG 2019: Short and Poster papers proceedings: 27. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p.51-60. | en |
| dc.identifier.isbn | 978-80-86943-38-1 (CD/-ROM) | |
| dc.identifier.issn | 2464–4617 (print) | |
| dc.identifier.issn | 2464-4625 (CD/DVD) | |
| dc.identifier.uri | http://hdl.handle.net/11025/35634 | |
| dc.format | 10 s. | cs |
| dc.format.mimetype | application/odt | |
| dc.language.iso | en | en |
| dc.publisher | Václav Skala - UNION Agency | cs |
| dc.rights | © Václav Skala - UNION Agency | cs |
| dc.subject | biometrická identifikace | cs |
| dc.subject | rozpoznávání hřbetních žil ruky | cs |
| dc.subject | architektury CNN | cs |
| dc.subject | konvoluční neuronové sítě | cs |
| dc.subject | přenos učení | cs |
| dc.title | CNN Approaches for Dorsal Hand Vein Based Identification | en |
| dc.type | konferenční příspěvek | cs |
| dc.type | conferenceObject | en |
| dc.rights.access | openAccess | en |
| dc.type.version | publishedVersion | en |
| dc.description.abstract-translated | In this paper we present a dorsal hand vein recognition method based on convolutional neural networks (CNN). We implemented and compared two CNNs trained from end-to-end to the most important state-of-the-art deep learning architectures (AlexNet, VGG, ResNet and SqueezeNet). We applied the transfer learning and finetuning techniques for the purpose of dorsal hand vein-based identification. The experiments carried out studied the accuracy and training behaviour of these network architectures. The system was trained and evaluated on the best-known database in this field, the NCUT, which contains low resolution, low contrast images. Therefore, different pre-processing steps were required, leading us to investigate the influence of a series of image quality enhancement methods such as Gaussian smoothing, inhomogeneity correction, contrast limited adaptive histogram equalization, ordinal image encoding, and coarse vein segmentation based on geometricalconsiderations. The results show high recognition accuracy for almost every such CNN-based setup. | en |
| dc.subject.translated | biometric identification | en |
| dc.subject.translated | dorsal hand vein recognition | en |
| dc.subject.translated | CNN architectures | en |
| dc.subject.translated | convolutional neural networks | en |
| dc.subject.translated | transfer learning | en |
| dc.identifier.doi | https://doi.org/10.24132/CSRN.2019.2902.2.7 | |
| dc.type.status | Peer-reviewed | en |
| Vyskytuje se v kolekcích: | WSCG '2019: Short Papers Proceedings | |
Soubory připojené k záznamu:
| Soubor | Popis | Velikost | Formát | |
|---|---|---|---|---|
| Lefkovits.pdf | Plný text | 1,08 MB | Adobe PDF | Zobrazit/otevřít |
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