TY - JOUR
T1 - RMOBF-Net
T2 - Network for the Restoration of Motion and Optical Blurred Finger-Vein Images for Improving Recognition Accuracy
AU - Choi, Jiho
AU - Hong, Jin Seong
AU - Kim, Seung Gu
AU - Park, Chanhum
AU - Nam, Se Hyun
AU - Park, Kang Ryoung
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/11
Y1 - 2022/11
N2 - 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.
AB - 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.
KW - finger-vein recognition
KW - motion and optical blurred finger-vein image
KW - RMOBF-Net
UR - http://www.scopus.com/inward/record.url?scp=85141875664&partnerID=8YFLogxK
U2 - 10.3390/math10213948
DO - 10.3390/math10213948
M3 - Article
AN - SCOPUS:85141875664
SN - 2227-7390
VL - 10
JO - Mathematics
JF - Mathematics
IS - 21
M1 - 3948
ER -