Deep Learning-Based Wrist Vascular Biometric Recognition
The need for contactless vascular biometric systems has significantly increased. In recent years, deep learning has proven to be efficient for vein segmentation and matching. Palm and finger vein biometrics are well researched; however, research on wrist vein biometrics is limited. Wrist vein biomet...
| Asıl Yazarlar: | , , , |
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| Materyal Türü: | Makale |
| Dil: | English |
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MDPI AG
2023-03-01
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| Seri Bilgileri: | Sensors |
| Konular: | |
| Online Erişim: | https://www.mdpi.com/1424-8220/23/6/3132 |
| _version_ | 1827747685803753472 |
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| author | Felix Marattukalam Waleed Abdulla David Cole Pranav Gulati |
| author_facet | Felix Marattukalam Waleed Abdulla David Cole Pranav Gulati |
| author_sort | Felix Marattukalam |
| collection | DOAJ |
| description | The need for contactless vascular biometric systems has significantly increased. In recent years, deep learning has proven to be efficient for vein segmentation and matching. Palm and finger vein biometrics are well researched; however, research on wrist vein biometrics is limited. Wrist vein biometrics is promising due to it not having finger or palm patterns on the skin surface making the image acquisition process easier. This paper presents a deep learning-based novel low-cost end-to-end contactless wrist vein biometric recognition system. FYO wrist vein dataset was used to train a novel U-Net CNN structure to extract and segment wrist vein patterns effectively. The extracted images were evaluated to have a Dice Coefficient of 0.723. A CNN and Siamese Neural Network were implemented to match wrist vein images obtaining the highest F1-score of 84.7%. The average matching time is less than 3 s on a Raspberry Pi. All the subsystems were integrated with the help of a designed GUI to form a functional end-to-end deep learning-based wrist biometric recognition system. |
| first_indexed | 2024-03-11T05:56:07Z |
| format | Article |
| id | doaj.art-4564361dde4a4a9db5b4a1dd62be4f2b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:56:07Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4564361dde4a4a9db5b4a1dd62be4f2b2023-11-17T13:46:26ZengMDPI AGSensors1424-82202023-03-01236313210.3390/s23063132Deep Learning-Based Wrist Vascular Biometric RecognitionFelix Marattukalam0Waleed Abdulla1David Cole2Pranav Gulati3Department of Electrical, Computer and Software Engineering, The University of Auckland, Auckland 1010, New ZealandDepartment of Electrical, Computer and Software Engineering, The University of Auckland, Auckland 1010, New ZealandDepartment of Electrical, Computer and Software Engineering, The University of Auckland, Auckland 1010, New ZealandDepartment of Electrical, Computer and Software Engineering, The University of Auckland, Auckland 1010, New ZealandThe need for contactless vascular biometric systems has significantly increased. In recent years, deep learning has proven to be efficient for vein segmentation and matching. Palm and finger vein biometrics are well researched; however, research on wrist vein biometrics is limited. Wrist vein biometrics is promising due to it not having finger or palm patterns on the skin surface making the image acquisition process easier. This paper presents a deep learning-based novel low-cost end-to-end contactless wrist vein biometric recognition system. FYO wrist vein dataset was used to train a novel U-Net CNN structure to extract and segment wrist vein patterns effectively. The extracted images were evaluated to have a Dice Coefficient of 0.723. A CNN and Siamese Neural Network were implemented to match wrist vein images obtaining the highest F1-score of 84.7%. The average matching time is less than 3 s on a Raspberry Pi. All the subsystems were integrated with the help of a designed GUI to form a functional end-to-end deep learning-based wrist biometric recognition system.https://www.mdpi.com/1424-8220/23/6/3132biometricswrist veindeep learningmachine learningSiamese Neural Networkconvolutional neural network |
| spellingShingle | Felix Marattukalam Waleed Abdulla David Cole Pranav Gulati Deep Learning-Based Wrist Vascular Biometric Recognition Sensors biometrics wrist vein deep learning machine learning Siamese Neural Network convolutional neural network |
| title | Deep Learning-Based Wrist Vascular Biometric Recognition |
| title_full | Deep Learning-Based Wrist Vascular Biometric Recognition |
| title_fullStr | Deep Learning-Based Wrist Vascular Biometric Recognition |
| title_full_unstemmed | Deep Learning-Based Wrist Vascular Biometric Recognition |
| title_short | Deep Learning-Based Wrist Vascular Biometric Recognition |
| title_sort | deep learning based wrist vascular biometric recognition |
| topic | biometrics wrist vein deep learning machine learning Siamese Neural Network convolutional neural network |
| url | https://www.mdpi.com/1424-8220/23/6/3132 |
| work_keys_str_mv | AT felixmarattukalam deeplearningbasedwristvascularbiometricrecognition AT waleedabdulla deeplearningbasedwristvascularbiometricrecognition AT davidcole deeplearningbasedwristvascularbiometricrecognition AT pranavgulati deeplearningbasedwristvascularbiometricrecognition |