The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Convolutional Neural Network for Finger-Vein-Based Biometric Identification
Abstract
The use of human finger-vein traits for the purpose of automatic user recognition has gained a lot of attention in recent years. Current state-of-the-art techniques can provide relatively good performance, yet they are strongly dependent upon the quality of the analyzed finger-vein images. In this paper, we propose a convolutional-neural-network-based finger-vein identification system and investigate the capabilities of the designed network over four publicly available databases. The main purpose of this paper is to propose a deep-learning method for finger-vein identification, which is able to achieve stable and highly accurate performance when dealing with finger-vein images of different quality. The reported extensive set of experiments show that the accuracy achievable with the proposed approach can go beyond 95% correct identification rate for all the four considered publicly available databases.
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Cites
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Extraction of Finger-Vein Patterns Using Maximum Curvature Points in Image Profiles
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Human Identification Using Finger Images
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Cited by
An Automated Biometric Identification System Using CNN-Based Palm Vein Recognition
Jhong, S.-Y., Tseng, P.-Y., Siriphockpirom, N., Hsia, C.-H., Huang, M.-S., Hua, K.-L., & Chen, Y.-Y. (2020) · 2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS) · doi:10.1109/aris50834.2020.9205778 · Cited by 31
VID: Human identification through vein patterns captured from commodity depth cameras
Shah, S. W., Kanhere, S. S., Zhang, J., & Yao, L. (2021) · IET Biometrics · doi:10.1049/bme2.12009 · Cited by 5
“As the pooling layer does not change the depth of its input, the output of P1 is of order [57, 57, 20].”
Background
Metadata
- Authors
- Das, R., Piciucco, E., Maiorana, E., & Campisi, P.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2019-02
- Volume
- 14
- Issue
- 2
- Pages
- 360-373
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2811014381
- Cited by
- 313
- References
- 39
- FWCI
- 23.23
- Type
- journal article
- Language
- en
- Open access
- closed
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- Das, R., Piciucco, E., Maiorana, E., & Campisi, P. (2019). Convolutional Neural Network for Finger-Vein-Based Biometric Identification. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/tifs.2018.2850320
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