A Comparative Study of Cross-Device Finger Vein Recognition Using Classical and Deep Learning Approaches
Finger vein recognition is gaining popularity in the field of biometrics, yet the inter-operability of finger vein patterns has received limited attention. This study aims to fill this gap by introducing a cross-device finger vein dataset and evaluating the performance of finger vein recognition acr...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | IET Biometrics |
| Online Access: | http://dx.doi.org/10.1049/2024/3236602 |
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| author | Tuğçe Arıcan Raymond Veldhuis Luuk Spreeuwers Loïc Bergeron Christoph Busch Ehsaneddin Jalilian Christof Kauba Simon Kirchgasser Sébastien Marcel Bernhard Prommegger Kiran Raja Raghavendra Ramachandra Andreas Uhl |
| author_facet | Tuğçe Arıcan Raymond Veldhuis Luuk Spreeuwers Loïc Bergeron Christoph Busch Ehsaneddin Jalilian Christof Kauba Simon Kirchgasser Sébastien Marcel Bernhard Prommegger Kiran Raja Raghavendra Ramachandra Andreas Uhl |
| author_sort | Tuğçe Arıcan |
| collection | DOAJ |
| description | Finger vein recognition is gaining popularity in the field of biometrics, yet the inter-operability of finger vein patterns has received limited attention. This study aims to fill this gap by introducing a cross-device finger vein dataset and evaluating the performance of finger vein recognition across devices using a classical method, a convolutional neural network, and our proposed patch-based convolutional auto-encoder (CAE). The findings emphasise the importance of standardisation of finger vein recognition, similar to that of fingerprints or irises, crucial for achieving inter-operability. Despite the inherent challenges of cross-device recognition, the proposed CAE architecture in this study demonstrates promising results in finger vein recognition, particularly in the context of cross-device comparisons. |
| format | Article |
| id | doaj-art-068612a31caa4ac587a134935dda2508 |
| institution | Kabale University |
| issn | 2047-4946 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-068612a31caa4ac587a134935dda25082025-02-03T06:14:53ZengWileyIET Biometrics2047-49462024-01-01202410.1049/2024/3236602A Comparative Study of Cross-Device Finger Vein Recognition Using Classical and Deep Learning ApproachesTuğçe Arıcan0Raymond Veldhuis1Luuk Spreeuwers2Loïc Bergeron3Christoph Busch4Ehsaneddin Jalilian5Christof Kauba6Simon Kirchgasser7Sébastien Marcel8Bernhard Prommegger9Kiran Raja10Raghavendra Ramachandra11Andreas Uhl12Faculty of Electrical EngineeringFaculty of Electrical EngineeringFaculty of Electrical EngineeringDepartment of Computer ScienceDepartment of Computer ScienceDepartment of Computer SciencesDepartment of Computer SciencesDepartment of Computer SciencesIDIAP Research InstituteDepartment of Computer SciencesDepartment of Computer ScienceDepartment of Computer ScienceDepartment of Computer SciencesFinger vein recognition is gaining popularity in the field of biometrics, yet the inter-operability of finger vein patterns has received limited attention. This study aims to fill this gap by introducing a cross-device finger vein dataset and evaluating the performance of finger vein recognition across devices using a classical method, a convolutional neural network, and our proposed patch-based convolutional auto-encoder (CAE). The findings emphasise the importance of standardisation of finger vein recognition, similar to that of fingerprints or irises, crucial for achieving inter-operability. Despite the inherent challenges of cross-device recognition, the proposed CAE architecture in this study demonstrates promising results in finger vein recognition, particularly in the context of cross-device comparisons.http://dx.doi.org/10.1049/2024/3236602 |
| spellingShingle | Tuğçe Arıcan Raymond Veldhuis Luuk Spreeuwers Loïc Bergeron Christoph Busch Ehsaneddin Jalilian Christof Kauba Simon Kirchgasser Sébastien Marcel Bernhard Prommegger Kiran Raja Raghavendra Ramachandra Andreas Uhl A Comparative Study of Cross-Device Finger Vein Recognition Using Classical and Deep Learning Approaches IET Biometrics |
| title | A Comparative Study of Cross-Device Finger Vein Recognition Using Classical and Deep Learning Approaches |
| title_full | A Comparative Study of Cross-Device Finger Vein Recognition Using Classical and Deep Learning Approaches |
| title_fullStr | A Comparative Study of Cross-Device Finger Vein Recognition Using Classical and Deep Learning Approaches |
| title_full_unstemmed | A Comparative Study of Cross-Device Finger Vein Recognition Using Classical and Deep Learning Approaches |
| title_short | A Comparative Study of Cross-Device Finger Vein Recognition Using Classical and Deep Learning Approaches |
| title_sort | comparative study of cross device finger vein recognition using classical and deep learning approaches |
| url | http://dx.doi.org/10.1049/2024/3236602 |
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