RMOBF-Net: Network for the Restoration of Motion and Optical Blurred Finger-Vein Images for Improving Recognition Accuracy

Jiho Choi, Jin Seong Hong, Seung Gu Kim, Chanhum Park, Se Hyun Nam and Kang Ryoung Park ()
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Jiho Choi: Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
Jin Seong Hong: Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
Seung Gu Kim: Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
Chanhum Park: Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
Se Hyun Nam: Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
Kang Ryoung Park: Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea

Mathematics, 2022, vol. 10, issue 21, 1-42

Abstract: Biometrics is a method of recognizing a person based on one or more unique physical and behavioral characteristics. Since each person has a different structure and shape, it is highly secure and more convenient than the existing security system. Among various biometric authentication methods, finger-vein recognition has advantages in that it is difficult to forge because a finger-vein exists inside one’s finger and high user convenience because it uses a non-invasive device. However, motion and optical blur may occur for some reasons such as finger movement and camera defocusing during finger-vein recognition, and such blurring occurrences may increase finger-vein recognition error. However, there has been no research on finger-vein recognition considering both motion and optical blur. Therefore, in this study, we propose a new method for increasing finger-vein recognition accuracy based on a network for the restoration of motion and optical blurring in a finger-vein image (RMOBF-Net). Our proposed network continuously maintains features that can be utilized during motion and optical blur restoration by actively using residual blocks and feature concatenation. Also, the architecture RMOBF-Net is optimized to the finger-vein image domain. Experimental results are based on two open datasets, the Shandong University homologous multi-modal traits finger-vein database and the Hong Kong Polytechnic University finger-image database version 1, from which equal error rates of finger-vein recognition accuracy of 4.290–5.779% and 2.465–6.663% were obtained, respectively. Higher performance was obtained from the proposed method compared with that of state-of-the-art methods.

Keywords: RMOBF-Net; motion and optical blurred finger-vein image; finger-vein recognition (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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