An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification
Finger vein biometrics is one of the most promising ways to identify a person because it can provide uniqueness, protection against forgery, and bioassay. Due to the limitations of the imaging environments, however, the finger vein images that are taken can quickly become low-contrast, blurry, and v...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IIETA
2023
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| Online Access: | http://eprints.uthm.edu.my/8876/ http://eprints.uthm.edu.my/8876/1/J15753_aac300d0e453cca2bcb3c5f096e94385.pdf |
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| author | Mustafa Sulaiman, Dawlat Abdulazeez, Adnan Mohsin Asaad Zebari, Dilovan Qader Zeebaree, Diyar A. Mostaf, Salama Saleem Sadiq, Shereen |
| author_facet | Mustafa Sulaiman, Dawlat Abdulazeez, Adnan Mohsin Asaad Zebari, Dilovan Qader Zeebaree, Diyar A. Mostaf, Salama Saleem Sadiq, Shereen |
| author_sort | Mustafa Sulaiman, Dawlat |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Finger vein biometrics is one of the most promising ways to identify a person because it can provide uniqueness, protection against forgery, and bioassay. Due to the limitations of the imaging environments, however, the finger vein images that are taken can quickly become low-contrast, blurry, and very noisy. Therefore, more robust and relevant feature extraction from the finger vein images is still open research that should be addressed. In this paper, we
propose a new technique of deep learning that is based on the attention mechanisms for human finger vein image identification and recognition and is called deep regional learning. Our proposed model relies on an unsupervised learning method that depends on optimized K-Means clustering for localized finger vein mask generation. The generated binary mask is used to build our attention learning model by making the deep learning structure focus on
the region-of-interest (ROI) learning instead of learning the whole feature domain. This technique makes the Deep Regional Attention Model learn more significant features with
less time and computational resources than the regular deep learning model. For experimental validation, we used different finger vein imaging datasets that have been
extracted and generated using our model. Original finger vein images, localized finger vein images (with no background), localized grayscale finger vein images (grayscale images with
no background and projected finger vein lines), and localized colored finger vein images (colored images with no background and projected finger vein lines) are used to train and test our model, which gets better results than traditional deep learning and other methods. |
| first_indexed | 2025-11-15T20:27:30Z |
| format | Article |
| id | uthm-8876 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:27:30Z |
| publishDate | 2023 |
| publisher | IIETA |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-88762023-06-18T01:32:40Z http://eprints.uthm.edu.my/8876/ An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification Mustafa Sulaiman, Dawlat Abdulazeez, Adnan Mohsin Asaad Zebari, Dilovan Qader Zeebaree, Diyar A. Mostaf, Salama Saleem Sadiq, Shereen T Technology (General) Finger vein biometrics is one of the most promising ways to identify a person because it can provide uniqueness, protection against forgery, and bioassay. Due to the limitations of the imaging environments, however, the finger vein images that are taken can quickly become low-contrast, blurry, and very noisy. Therefore, more robust and relevant feature extraction from the finger vein images is still open research that should be addressed. In this paper, we propose a new technique of deep learning that is based on the attention mechanisms for human finger vein image identification and recognition and is called deep regional learning. Our proposed model relies on an unsupervised learning method that depends on optimized K-Means clustering for localized finger vein mask generation. The generated binary mask is used to build our attention learning model by making the deep learning structure focus on the region-of-interest (ROI) learning instead of learning the whole feature domain. This technique makes the Deep Regional Attention Model learn more significant features with less time and computational resources than the regular deep learning model. For experimental validation, we used different finger vein imaging datasets that have been extracted and generated using our model. Original finger vein images, localized finger vein images (with no background), localized grayscale finger vein images (grayscale images with no background and projected finger vein lines), and localized colored finger vein images (colored images with no background and projected finger vein lines) are used to train and test our model, which gets better results than traditional deep learning and other methods. IIETA 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/8876/1/J15753_aac300d0e453cca2bcb3c5f096e94385.pdf Mustafa Sulaiman, Dawlat and Abdulazeez, Adnan Mohsin and Asaad Zebari, Dilovan and Qader Zeebaree, Diyar and A. Mostaf, Salama and Saleem Sadiq, Shereen (2023) An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification. Traitement du Signal, 39 (6). pp. 1991-2002. https://doi.org/10.18280/ts.390611 |
| spellingShingle | T Technology (General) Mustafa Sulaiman, Dawlat Abdulazeez, Adnan Mohsin Asaad Zebari, Dilovan Qader Zeebaree, Diyar A. Mostaf, Salama Saleem Sadiq, Shereen An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification |
| title | An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification |
| title_full | An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification |
| title_fullStr | An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification |
| title_full_unstemmed | An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification |
| title_short | An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification |
| title_sort | attention-based deep regional learning model for enhanced finger vein identification |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/8876/ http://eprints.uthm.edu.my/8876/ http://eprints.uthm.edu.my/8876/1/J15753_aac300d0e453cca2bcb3c5f096e94385.pdf |