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Sensors 2017, 17(6), 1297; doi:10.3390/s17061297

Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors

Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea
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Academic Editor: Vittorio M. N. Passaro
Received: 11 May 2017 / Revised: 1 June 2017 / Accepted: 1 June 2017 / Published: 6 June 2017
(This article belongs to the Section Physical Sensors)
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Abstract

Conventional finger-vein recognition systems perform recognition based on the finger-vein lines extracted from the input images or image enhancement, and texture feature extraction from the finger-vein images. In these cases, however, the inaccurate detection of finger-vein lines lowers the recognition accuracy. In the case of texture feature extraction, the developer must experimentally decide on a form of the optimal filter for extraction considering the characteristics of the image database. To address this problem, this research proposes a finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN). In the experiments using the two finger-vein databases constructed in this research and the SDUMLA-HMT finger-vein database, which is an open database, the method proposed in this research showed a better performance compared to the conventional methods. View Full-Text
Keywords: biometrics; finger-vein recognition; texture feature extraction; CNN biometrics; finger-vein recognition; texture feature extraction; CNN
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Hong, H.G.; Lee, M.B.; Park, K.R. Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors. Sensors 2017, 17, 1297.

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