Hybrid Feature Extractor Using Discrete Wavelet Transform and Histogram of Oriented Gradient on Convolutional-Neural-Network-Based Palm Vein Recognition.
- Publisher:
- MDPI
- Publication Type:
- Journal Article
- Citation:
- Sensors (Basel), 2024, 24, (2), pp. 341
- Issue Date:
- 2024-01-06
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Hybrid Feature Extractor Using Discrete Wavelet Transform and Histogram of Oriented Gradient on Convolutional-Neural-Network.pdf | Published version | 5.01 MB | Adobe PDF |
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Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Wulandari, M | |
dc.contributor.author |
Chai, R |
|
dc.contributor.author | Basari, B | |
dc.contributor.author | Gunawan, D | |
dc.date.accessioned | 2024-08-02T02:42:44Z | |
dc.date.available | 2023-12-30 | |
dc.date.available | 2024-08-02T02:42:44Z | |
dc.date.issued | 2024-01-06 | |
dc.identifier.citation | Sensors (Basel), 2024, 24, (2), pp. 341 | |
dc.identifier.issn | 1424-8220 | |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/10453/179996 | |
dc.description.abstract | Biometric recognition techniques have become more developed recently, especially in security and attendance systems. Biometrics are features attached to the human body that are considered safer and more reliable since they are difficult to imitate or lose. One of the popular biometrics considered in research is palm veins. They are an intrinsic biometric located under the human skin, so they have several advantages when developing verification systems. However, palm vein images obtained based on infrared spectra have several disadvantages, such as nonuniform illumination and low contrast. This study, based on a convolutional neural network (CNN), was conducted on five public datasets from CASIA, Vera, Tongji, PolyU, and PUT, with three parameters: accuracy, AUC, and EER. Our proposed VeinCNN recognition method, called verification scheme with VeinCNN, uses hybrid feature extraction from a discrete wavelet transform (DWT) and histogram of oriented gradient (HOG). It shows promising results in terms of accuracy, AUC, and EER values, especially in the total parameter values. The best result was obtained for the CASIA dataset with 99.85% accuracy, 99.80% AUC, and 0.0083 EER. | |
dc.format | Electronic | |
dc.language | eng | |
dc.publisher | MDPI | |
dc.relation.ispartof | Sensors (Basel) | |
dc.relation.isbasedon | 10.3390/s24020341 | |
dc.rights | info:eu-repo/semantics/restrictedAccess | |
dc.subject | 0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering | |
dc.subject.classification | Analytical Chemistry | |
dc.subject.classification | 3103 Ecology | |
dc.subject.classification | 4008 Electrical engineering | |
dc.subject.classification | 4009 Electronics, sensors and digital hardware | |
dc.subject.classification | 4104 Environmental management | |
dc.subject.classification | 4606 Distributed computing and systems software | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Wavelet Analysis | |
dc.subject.mesh | Hand | |
dc.subject.mesh | Biometry | |
dc.subject.mesh | Light | |
dc.subject.mesh | Neural Networks, Computer | |
dc.subject.mesh | Hand | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Biometry | |
dc.subject.mesh | Light | |
dc.subject.mesh | Wavelet Analysis | |
dc.subject.mesh | Neural Networks, Computer | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Wavelet Analysis | |
dc.subject.mesh | Hand | |
dc.subject.mesh | Biometry | |
dc.subject.mesh | Light | |
dc.subject.mesh | Neural Networks, Computer | |
dc.title | Hybrid Feature Extractor Using Discrete Wavelet Transform and Histogram of Oriented Gradient on Convolutional-Neural-Network-Based Palm Vein Recognition. | |
dc.type | Journal Article | |
utslib.citation.volume | 24 | |
utslib.location.activity | Switzerland | |
utslib.for | 0301 Analytical Chemistry | |
utslib.for | 0502 Environmental Science and Management | |
utslib.for | 0602 Ecology | |
utslib.for | 0805 Distributed Computing | |
utslib.for | 0906 Electrical and Electronic Engineering | |
pubs.organisational-group | University of Technology Sydney | |
pubs.organisational-group | University of Technology Sydney/Faculty of Engineering and Information Technology | |
pubs.organisational-group | University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering | |
utslib.copyright.status | recently_added | * |
dc.date.updated | 2024-08-02T02:42:37Z | |
pubs.issue | 2 | |
pubs.publication-status | Published online | |
pubs.volume | 24 | |
utslib.citation.issue | 2 |
Abstract:
Biometric recognition techniques have become more developed recently, especially in security and attendance systems. Biometrics are features attached to the human body that are considered safer and more reliable since they are difficult to imitate or lose. One of the popular biometrics considered in research is palm veins. They are an intrinsic biometric located under the human skin, so they have several advantages when developing verification systems. However, palm vein images obtained based on infrared spectra have several disadvantages, such as nonuniform illumination and low contrast. This study, based on a convolutional neural network (CNN), was conducted on five public datasets from CASIA, Vera, Tongji, PolyU, and PUT, with three parameters: accuracy, AUC, and EER. Our proposed VeinCNN recognition method, called verification scheme with VeinCNN, uses hybrid feature extraction from a discrete wavelet transform (DWT) and histogram of oriented gradient (HOG). It shows promising results in terms of accuracy, AUC, and EER values, especially in the total parameter values. The best result was obtained for the CASIA dataset with 99.85% accuracy, 99.80% AUC, and 0.0083 EER.
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