1. Home
  2. Issues
  3. Volume 32, Issue 4 (2021)
  4. Palm Vein Recognition Based on Convoluti ...

Informatica


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.

References

 
Aglio-Caballero, A., Ríos-Sánchez, B., Sánchez-Ávila, C., de Giles, M.J.M. (2017). Analysis of local binary patterns and uniform local binary patterns for palm vein biometric recognition. In: International Carnahan Conference on Security Technology (ICCST), pp. 1–6.
 
Akinsowon, O.A., Alese, B.K. (2013). Edge detection methods in palm-print identification. In: 8th International Conference for Internet Technology and Secured Transactions (ICITST-2013), pp. 422–426.
 
Badrinath, G.S., Gupta, P., Mehrotra, H. (2013). Score level fusion of voting strategy of geometric hashing and SURF for an efficient palmprint-based identification. Journal of Real-Time Image Processing, 8(3), 265–284.
 
Cancian, P., Donato, G.W.D., Rana, V., Santambrogio, M.D. (2017). An embedded Gabor-based palm vein recognition system. In: IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp. 405–408.
 
Choras, R.S. (2017). Personal identification using forearm vein patterns. In: International Conference and Workshop on Bioinspired Intelligence (IWOBI), pp. 1–5.
 
Fronitasari, D., Gunawan, D. (2017). Palm vein recognition by using modified of local binary pattern (LBP) for extraction feature. In: 15th International Conference on Quality in Research (QiR): International Symposium on Electrical and Computer Engineering, pp. 18–22.
 
Garg, S.N., Vig, R., Gupta, S. (2016). Multimodal biometric system based on decision level fusion. In: International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), pp. 753–758.
 
Gurunathan, V., Sathiyapriya, T., Sudhakar, R. (2016). Multimodal biometric recognition system using SURF algorithm. In: 10th International Conference on Intelligent Systems and Control (ISCO), pp. 1–5.
 
Hao, Y., Sun, Z., Tan, T., Ren, C. (2008). Multispectral palm image fusion for accurate contact-free palmprint recognition. In: 15th IEEE International Conference on Image Processing, pp. 281–284.
 
Huang, H., Liu, S., Zheng, H., Ni, L., Zhang, Y., Li, W. (2017). DeepVein: novel finger vein verification methods based on deep convolutional neural networks. In: IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), pp. 1–8.
 
Jain, A.K., Bolle, R., Pankanti, S. (2006). Biometrics: Personal Identification in networked Society. Springer Science & Business Media, Vol. 479.
 
Kang, W., Wu, Q. (2014). Contactless palm vein recognition using a mutual foreground-based local binary pattern. IEEE Transactions on Information Forensics and Security, 9(11), 1974–1985.
 
Khan, M.H.-M., Subramanian, R.K., Khan, Ali Mamode, N. (2009). Representation of hand dorsal vein features using a low dimensional representation integrating cholesky decomposition. In: 2nd International Congress on Image and Signal Processing, pp. 1–6.
 
Kim, Y., Toh, K.A., Teoh, A.B.J. (2010). An online learning algorithm for biometric scores fusion. In: Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–6.
 
Kong, A., Zhang, D., Kamel, M. (2009). A survey of palmprint recognition. Pattern Recognition, 42(7), 1408–1418.
 
Krizhevsky, A., Sutskever, I., Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105.
 
Lécun, Y., Bottou, L., Bengio, Y., Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.
 
Lee, J.C. (2012). A novel biometric system based on palm vein image. Pattern Recognition Letters, 33(12), 1520–1528.
 
Lin, Y., Du, E.Y., Zhou, Z., Thomas, N.L. (2014). An efficient parallel approach for sclera vein recognition. IEEE Transactions on Information Forensics and Security, 9(2), 147–157.
 
Liu, J., Zhang, Y. (2011). Palm-dorsa vein recognition based on two-dimensional fisher linear discriminant. In: International Conference on Image Analysis and Signal Processing, pp. 550–552.
 
Lu, W., Yuan, W.Q. (2017). Comparison of four local invariant characteristics based on palm vein. In: IEEE International Conference on Computational Science and Engineering (CSE) and Embedded and Ubiquitous Computing (EUC), pp. 850–853.
 
Mohamed, C., Akhtar, Z., Eddine, B.N., Falk, T.H. (2017). Combining left and right wrist vein images for personal verification. In: Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1–6.
 
O’Gorman, L. (2003). Comparing passwords, tokens, and biometrics for user authentication. Proceedings of the IEEE 91(12), 2021–2040.
 
Pan, S.J., Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
 
Pascual, J.E.S., Uriarte-Antonio, J., Sanchez-Reillo, R., Lorenz, M.G. (2010). Capturing hand or wrist vein images for biometric authentication using low-cost devices. In: Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 318–322.
 
Pratiwi, A.Y., Budi, W.T.A., Ramadhani, K.N. (2016). Identity recognition with palm vein feature using local binary pattern rotation Invariant. In: 4th International Conference on Information and Communication Technology (ICoICT), pp. 1–6.
 
Raghavendra, R., Surbiryala, J., Busch, C. (2015). Hand dorsal vein recognition: sensor, algorithms and evaluation. In: IEEE International Conference on Imaging Systems and Techniques (IST), pp. 1–6.
 
Raut, S.D., Humbe, V.T. (2015). Palm vein recognition system based on corner point detection. In: IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), pp. 499–502.
 
Seshikala, G., Kulkarni, U., Giriprasad, M.N. (2012). Biometric parameters & palm print recognition. International Journal of Computer Applications, 46(21), 31–34.
 
Shimizu, K. (1992). Optical trans-body imaging feasibility of optical CT and functional imaging of living body. Medicina Philosophica, 11, 620–629.
 
Suganya, A., Sivitha, M. (2014). A new biometric using sclera vein recognition for human identification. In: IEEE International Conference on Computational Intelligence and Computing Research, pp. 1–4.
 
Simonyan, K., Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556.
 
Vaid, S., Mishra, D. (2015). Comparative analysis of palm-vein recognition system using basic transforms. In: IEEE International Advance Computing Conference (IACC), pp. 1105–1110.
 
Xu, J. (2015). Palm vein identification based on partial least square. In: 2015 8th International Congress on Image and Signal Processing (CISP), pp. 670–674.
 
Zhang, D., Guo, Z., Lu, G., Zhang, L., Zuo, W. (2010). An online system of multispectral palmprint verification. IEEE Transactions on Instrumentation and Measurement, 59(2), 480–490.
 
Zheng, S.W., Han, J.G. (2018). Deep learning for contact-less palmprint recognition. Microelectronics & Computer, 35(4), 98–102.
 
Zheng, S.W., Han, J.G., Wang, Y.F., Jing, J.D. (2017). Convolutional neural network for palmprint recognition. Science Technology and Engineering, 17(35), 1–7.

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.


Full article Cited by PDF XML
Full article Cited by PDF XML

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.

Metrics
since January 2020
2663

Article info
views

1931

Full article
views

1443

PDF
downloads

219

XML
downloads


Share


RSS