IEICE TRANSACTIONS on Fundamentals
High Precision Fingerprint Verification for Small Area Sensor Based on Deep Learning
Summary :
The fingerprint verification system is widely used in mobile devices because of fingerprint's distinctive features and ease of capture. Typically, mobile devices utilize small sensors, which have limited area, to capture fingerprint. Meanwhile, conventional fingerprint feature extraction methods need detailed fingerprint information, which is unsuitable for those small sensors. This paper proposes a novel fingerprint verification method for small area sensors based on deep learning. A systematic method combines deep convolutional neural network (DCNN) in a Siamese network for feature extraction and XGBoost for fingerprint similarity training. In addition, a padding technique also introduced to avoid wraparound error problem. Experimental results show that the method achieves an improved accuracy of 66.6% and 22.6% in the FingerPassDB7 and FVC2006DB1B dataset, respectively, compared to the existing methods.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E107-A No.1 pp.157-168
- Publication Date
- 2024/01/01
- Publicized
- 2023/06/26
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2022EAP1079
- Type of Manuscript
- PAPER
- Category
- Biometrics
Authors
Nabilah SHABRINA
Tokyo Institute of Technology
Dongju LI
Tokyo Institute of Technology
Tsuyoshi ISSHIKI
Tokyo Institute of Technology
Keyword
Latest Issue
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Nabilah SHABRINA, Dongju LI, Tsuyoshi ISSHIKI, "High Precision Fingerprint Verification for Small Area Sensor Based on Deep Learning" in IEICE TRANSACTIONS on Fundamentals,
vol. E107-A, no. 1, pp. 157-168, January 2024, doi: 10.1587/transfun.2022EAP1079.
Abstract: The fingerprint verification system is widely used in mobile devices because of fingerprint's distinctive features and ease of capture. Typically, mobile devices utilize small sensors, which have limited area, to capture fingerprint. Meanwhile, conventional fingerprint feature extraction methods need detailed fingerprint information, which is unsuitable for those small sensors. This paper proposes a novel fingerprint verification method for small area sensors based on deep learning. A systematic method combines deep convolutional neural network (DCNN) in a Siamese network for feature extraction and XGBoost for fingerprint similarity training. In addition, a padding technique also introduced to avoid wraparound error problem. Experimental results show that the method achieves an improved accuracy of 66.6% and 22.6% in the FingerPassDB7 and FVC2006DB1B dataset, respectively, compared to the existing methods.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022EAP1079/_p
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@ARTICLE{e107-a_1_157,
author={Nabilah SHABRINA, Dongju LI, Tsuyoshi ISSHIKI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={High Precision Fingerprint Verification for Small Area Sensor Based on Deep Learning},
year={2024},
volume={E107-A},
number={1},
pages={157-168},
abstract={The fingerprint verification system is widely used in mobile devices because of fingerprint's distinctive features and ease of capture. Typically, mobile devices utilize small sensors, which have limited area, to capture fingerprint. Meanwhile, conventional fingerprint feature extraction methods need detailed fingerprint information, which is unsuitable for those small sensors. This paper proposes a novel fingerprint verification method for small area sensors based on deep learning. A systematic method combines deep convolutional neural network (DCNN) in a Siamese network for feature extraction and XGBoost for fingerprint similarity training. In addition, a padding technique also introduced to avoid wraparound error problem. Experimental results show that the method achieves an improved accuracy of 66.6% and 22.6% in the FingerPassDB7 and FVC2006DB1B dataset, respectively, compared to the existing methods.},
keywords={},
doi={10.1587/transfun.2022EAP1079},
ISSN={1745-1337},
month={January},}
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TY - JOUR
TI - High Precision Fingerprint Verification for Small Area Sensor Based on Deep Learning
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 157
EP - 168
AU - Nabilah SHABRINA
AU - Dongju LI
AU - Tsuyoshi ISSHIKI
PY - 2024
DO - 10.1587/transfun.2022EAP1079
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E107-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2024
AB - The fingerprint verification system is widely used in mobile devices because of fingerprint's distinctive features and ease of capture. Typically, mobile devices utilize small sensors, which have limited area, to capture fingerprint. Meanwhile, conventional fingerprint feature extraction methods need detailed fingerprint information, which is unsuitable for those small sensors. This paper proposes a novel fingerprint verification method for small area sensors based on deep learning. A systematic method combines deep convolutional neural network (DCNN) in a Siamese network for feature extraction and XGBoost for fingerprint similarity training. In addition, a padding technique also introduced to avoid wraparound error problem. Experimental results show that the method achieves an improved accuracy of 66.6% and 22.6% in the FingerPassDB7 and FVC2006DB1B dataset, respectively, compared to the existing methods.
ER -
English
