Palm Vein Recognition Based on Adaptive Region-of-Interest Segmentation and Modified Deep Learning Model
Authors: 
Liangbin Cheng, Ji Li, Gang Xu, Shanwen Guan, Xiaonan Luo, Lingling LiAuthors Info & Claims
Liangbin Cheng, Ji Li, Gang Xu, Shanwen Guan, Xiaonan Luo, Lingling LiAuthors Info & ClaimsPages 339 - 346
Abstract
Region of Interest (ROI) is the basis of palm vein recognition. The ROI segmentation methods based on key points location can provide an accurate ROI. However, these methods require the manual labeling of auxiliary points in advance and can't perform well in palm vein images that don't contain the whole fingers. To solve that, this paper proposes an adaptive palm vein recognition scheme:(1) The center of the ROI was located by the centroid of the binary palm vein image and calibrated by image erosion. The size of the ROI was determined by the maximized inscribed circle. The tangent point between the inscribed circle and hand contour was used as an auxiliary to calibrate ROI in angle. (2) A deep learning model that can receive input of any size is designed to extract vein features. Feature extraction can be implemented without scaling images to avoid image distortion. The research results and experiments show that this recognition scheme is superior to most traditional methods.
References
[1]
He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
[2]
He, K.; Zhang, X.; Ren, S.; Sun, J. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence. 2015, 37, 1904-1916
[3]
Zhou, Y.; Kumar, A. Human identification using palm-vein images. IEEE transactions on information forensics and security, 2011, 6, 1259–1274.
[4]
Lee, Y. P. Palm vein recognition based on a modified 2D2LDA. Signal, Image and Video Processing. 2015, 9, 229–242.
[5]
Kang, W.; Wu, Q. Contactless palm vein recognition using a mutual foreground-based local binary pattern. IEEE transactions on Information Forensics and Security. 2014, 9, 1974-1985.
[6]
Michael, G. K. O.; Connie, T.; Hoe, L. S.; Jin, A. T. B. Design and implementation of a contactless palm vein recognition system. Proceedings of the 2010 Symposium on Information and Communication Technology, 2010, pp. 92–99.
[7]
Lin, S.; Xu, T.; Yin, X. Region of interest extraction for palmprint and palm vein recognition. 2016 9th International Congress on Image and Signal Processing, Biomedical Engineering and Informatics (CISP-BMEI). IEEE, 2016, pp. 538–542.
[8]
Wu, W.; Elliott, S. J.; Lin, S.; Yuan, W. Low-cost biometric recognition system based on NIR palm vein image. IET Biometrics. 2019, 8, 206–214.
[9]
Zhang, H.; Hu, D. A palm vein recognition system. 2010 International Conference on Intelligent Computation Technology and Automation. IEEE, 2010, Vol. 1, pp. 285–288.
[10]
Raut, S. D.; Humbe, V. T. A novel approach for palm vein feature extraction using Gabor and canny edge detector. 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2015, pp. 1–4.
[11]
Wang, R.; Wang, G.; Chen, Z.; Zeng, Z.; Wang, Y. A palm vein identification system based on Gabor wavelet features. Neural Computing and Applications. 2014, 24, 161–168.
[12]
Mirmohamadsadeghi, L.; Drygajlo, A. Palm vein recognition with local texture patterns. Iet Biometrics. 2014, 3, 198–206.
[13]
Samai, D.; Meraoumia, A.; Bendjenna, H.; Laimeche, L. Oriented Local Binary Pattern (LBP): A new scheme for an efficient feature extraction technique. 2017 International Conference on Mathematics and Information Technology (ICMIT). IEEE, 2017, pp. 155–161.
[14]
Cancian, P.; Di Donato, G.W.; Rana, V.; Santambrogio, M.D. An embedded Gabor-based palm vein recognition system. 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 2017, pp. 405–408.
[15]
Wu, X.; Gao, E.; Tang, Y.; Wang, K. A novel biometric system based on hand vein. 2010 Fifth International Conference on Frontier of Computer Science and Technology. IEEE, 2010, pp. 522–526.
[16]
Ladoux, P. O.; Rosenberger, C.; Dorizzi, B. Palm vein verification system based on SIFT matching. International Conference on Biometrics. Springer, 2009, pp. 1290–1298.
[17]
Wu, K. S.; Lee, J. C.; Lo, T. M.; Chang, K. C.; Chang, C. P. A secure palm vein recognition system. Journal of Systems and Software. 2013, 86, 2870–2876.
[18]
Gurunathan, V.; Sathiyapriya, T.; Sudhakar, R. Multimodal biometric recognition system using SURF algorithm. 2016 10th International Conference on Intelligent Systems and Control (ISCO). IEEE, 2016, pp. 1–5.
[19]
Kang, W.; Liu, Y.; Wu, Q.; Yue, X. Contact-free palm-vein recognition based on local invariant features. PloS one. 2014, 9, e97548.
[20]
Rizki, F.; Wirayuda, T. A. B.; Ramadhani, K. N. Identity recognition based on palm vein feature using two-dimensional linear discriminant analysis. 2016 1st International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2016, pp. 21–25.
[21]
Zhang, L.; Cheng, Z.; Shen, Y.; Wang, D. Palmprint and palmvein recognition based on DCNN and a new large-scale contactless palmvein dataset. Symmetry. 2018, 10, 78.
[22]
Haouam, M.Y.; Meraoumia, A.; Laimeche, L.; Bendib, I. S-DCTNet: Security-oriented biometric feature extraction technique. Multimedia Tools and Applications. 2021, 80, 36059–36091.
[23]
Pan, M.; Kang, W. Palm vein recognition based on three local invariant feature extraction algorithms. Chinese Conference on Biometric Recognition. Springer, 2011, pp. 116–124.
[24]
Zhang, L.; Li, L.; Yang, A.; Shen, Y.; Yang, M. Towards contactless palmprint recognition: A novel device, a new benchmark, and a collaborative representation-based identification approach. Pattern Recognition. 2017, 69, 199–212.
[25]
Pratiwi, A. Y.; Budi, W. T. A.; Ramadhani, K. N. Identity recognition with palm vein feature using local binary pattern rotation Invariant. 2016 4th International Conference on Information and Communication Technology (ICoICT). IEEE, 2016, pp. 1–6.
[26]
Rajalakshmi, M.; Annapurani Panaiyappan, K. A multimodal architecture using Adapt-HKFCT segmentation and feature-based chaos integrated deep neural networks (Chaos-DNN-SPOA) for contactless biometricpalm vein recognition system. International Journal of Intelligent Systems. 2022, 37, 1846–1879.
[27]
Otsu, N. A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics. 1979, 9, 62–66.
[28]
Castleman, K. R. Digital image processing; Prentice Hall Press, 1996.
[29]
Zhong, D.; Zhu, J. Centralized large margin cosine loss for open-set deep palmprint recognition. IEEE Transactions on Circuits and Systems for Video Technology. 2019, 30, 1559–1568.
Recommendations
Biometric recognition using finger and palm vein images
In recent times, biometrics is the best alternative for the token-based and knowledge-based security systems. Out of the existing biometric modalities, the vascular biometric modalities are preferred for authenticating the person, because of its ...
Palm vein recognition using adaptive Gabor filter
Vein pattern recognition is one of the newest biometric techniques researched today. One of the reliable and robust personal identification authentication approaches using palm vein patterns is presented in this paper. In our work, we consider the palm ...
A secure palm vein recognition system
With the increasing needs in security systems, vein recognition is one of the important and reliable solutions of identity security for biometrics-based identification systems. The obvious and stable line-feature-based approach can be used to clearly ...
Comments
Information & Contributors
Information
Published In
December 2022
1396 pages
ISBN:9781450398343
DOI:10.1145/3584376
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 April 2023
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
RICAI 2022
RICAI 2022: 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence
December 16 - 18, 2022
Dongguan, China
Acceptance Rates
Overall Acceptance Rate 140 of 294 submissions, 48%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 62Total Downloads
- Downloads (Last 12 months)26
- Downloads (Last 6 weeks)1
Reflects downloads up to 20 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format