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Palm Vein Recognition Based on Convolutional Neural Network
Volume 32, Issue 4 (2021), pp. 687–708
Pub. online: 27 September 2021      Type: Research Article      Open accessOpen Access
Received
1 April 2020
Accepted
1 September 2021
Published
27 September 2021

Abstract

Convolutional neural networks (CNNs) were popular in ImageNet large scale visual recognition competition (ILSVRC 2012) because of their identification ability and computational efficiency. This paper proposes a palm vein recognition method based on CNN. The four main steps of palm vein recognition are image acquisition, image preprocessing, feature extraction, and matching. To reduce the processing steps in the recognition of palm vein images, a palm vein recognition method using a CNN is proposed. CNN is a deep learning network. Palm vein images are acquired using near-infrared light, under which the veins in the palm of the hand are relatively prominent. To obtain a good vein image, many previous methods used preprocessing to further enhance the image before using feature extraction to find feature matches for further comparison. In recent years, CNNs have been shown to have great advantages and have performed well in image classification. To reduce early-stage image processing, a CNN is used to classify and recognize palm vein images. The networks AlexNet and VGG depth CNN were trained to extract image features. The palm vein recognition rates by VGG-19, VGG-16, and AlexNet were 98.5%, 97.5%, and 96%, respectively.

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Biographies

Fanjiang Yong-Yi
yyfanj@csie.fju.edu.tw

Y.-Y. Fanjiang received his PhD degrees in computer science and information engineering from National Central University, Taiwan, in 2004. Currently, he is a professor in the Department of Computer Science and Information Engineering, and the director of Information Technology Center, Fu Jen Catholic University. His research interests include service-oriented computing, software engineering, semantic web, and artificial intelligence in internet of things. He is a member of IEEE.

Lee Cheng-Chi
https://orcid.org/0000-0002-8918-1703
cclee@mail.fju.edu.tw

C.-C. Lee received the PhD degree in computer science from National Chung Hsing University (NCHU), Taiwan, in 2007. He is currently a distinguished professor with the Department of Library and Information Science at Fu Jen Catholic University. Dr. Lee is currently an editorial board member of Mathematics, Electronics, Future Internet, International Journal of Network Security, Journal of Computer Science, Cryptography, International Journal of Internet Technology and Secured Transactions, Journal of Library and Information Studies, Journal of InfoLib and Archives, and guest editor of Sensors. He also served as a reviewer of many SCI-index journals, other journals and conferences. His current research interests include data security, cryptography, network security, mobile communications and computing, wireless communications. Dr. Lee had published over 200 scientific articles on the above research fields in international journals and conferences. He is a member of IEEE, the Chinese Cryptology and Information Security Association (CCISA), the Library Association of The Republic of China, and the ROC Phi Tau Phi Scholastic Honor Society.

Du Yan-Ta

Y.-T. Du received the BS and MS in computer science and information engineering, Fu Jen Catholic University, New Taipei City 24205, Taiwan, R.O.C., in 2016 and 2018. His current research interests include information security, cryptography, and mobile communications.

Horng Shi-Jinn
horngsj@yahoo.com.tw

S.-J. Horng (also known as Hsi-Chin Hung) received the BS degree in electronics engineering from National Taiwan Institute of Technology, Taipei, the MS degree in information engineering from National Central University, Taiwan, and the PhD degree in computer science from National Tsing Hua University, Taiwan, in 1980, 1984, and 1989, respectively. He was the Dean of the College of Electrical Engineering and Computer Science, National United University, Taiwan. Currently, he is a chair professor in the Department of Computer Science and Information Engineering, NTUST. He was a visiting professor at Tokyo Institute of Technology, in 2008; Georgia State University, in 2007; University of Dayton, Ohio, in 2000; National Mongolia University, in 2004; Southwest Jiaotong University, in 2004. He also worked as a PMTS at AT&T Bell Laboratories from 1990 to 1991. His research interests include deep learning, biometric recognition, multi-medium, image processing, and information security. He has published more than 200 research papers and received many awards; especially, the Distinguished Research Award between 2004 and 2006 from the National Science Council in Taiwan.


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Copyright
© 2021 Vilnius University
Open access article under the CC BY license.

Keywords
palm vein recognition CNN AlexNet VGG biometrics

Funding
This research was partially supported by the Ministry of Science and Technology (MOST), Taiwan, R.O.C., under contract no.: MOST 108-2410-H-030-074 and 110-2410-H-030-032. This work was also partially supported by the “Center for Cyber-Physical System Innovation” from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan and MOST under 109-2221-E-011-115, 110-2221-E-011-125, 110-2218-E-011-006-MBK.

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