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January  2017, 2(1): 59-68. doi: 10.3934/bdia.2017008

Two-hidden-layer extreme learning machine based wrist vein recognition system

1. 

Zhengzhou University, Zhengzhou, Henan, China

2. 

Zhongyuan University of Technology, Zhengzhou, Henan, China

* Corresponding author: liangjing@zzu.edu.cn

Published  September 2017

Fund Project: The first author is supported by National Natural Science Foundation of China (61473266,61673404,61305080, and U1304602)

Vein recognition is a new identity authentication technology. It attracts many researchers' attention due to its good security and reliability. This paper proposes a wrist vein recognition system. The proposed system identifies people according to the characteristics of their wrist veins. A special camera is reformed to obtain the wrist vein images and an image dataset is established. Principal component analysis (PCA) is adopted to eliminate the redundant information in the images and extract their global features. The global features are classified by Two-hidden-layer Extreme Learning Machine (TELM). TELM is compared with original Extreme Learning Machine (ELM) and other two algorithms Support Vector Machine (SVM) and Naive Bayes (NB). Experiment results show that the accuracy of the proposed system is higher than the other three algorithms. Though the speed of TELM is not the fastest, it is able to recognize images within satisfactory time.

Citation: Cai-Tong Yue, Jing Liang, Bo-Fei Lang, Bo-Yang Qu. Two-hidden-layer extreme learning machine based wrist vein recognition system. Big Data & Information Analytics, 2017, 2 (1) : 59-68. doi: 10.3934/bdia.2017008
References:
[1]

T. ChenJ. Cai and L. Guo, Research on hybrid face recognition algorithm based on voting Extreme Learning Machine, Journal of Zhengzhou University (Engineering Science), 37 (2016), 37-41. Google Scholar

[2]

J. M. Cross and C. L. Smith, Thermographic imaging of the subcutaneous vascular network of the back of the hand for biometric identification, Institute of Electrical and Electronics Engineers Conference on Security Technology, (1995), 20-35. doi: 10.1109/CCST.1995.524729. Google Scholar

[3]

S. DongJ. Yang and Y. Chen, Finger vein recognition based on multi-orientation weighted symmetric local graph structure, KSII Transactions on Internet and Information Systems, 9 (2015), 4126-4142. Google Scholar

[4]

J. Hashimoto, Finger vein authentication technology and its future, Symposium on VLSI Circuits, (2006), 5-8. doi: 10.1109/VLSIC.2006.1705285. Google Scholar

[5]

C. B. HsuS. S. Hao and J. C. Lee, Personal authentication through dorsal hand vein patterns, Optical Engineering, 50 (2011), 087201, 10pp. doi: 10.1117/1.3607413. Google Scholar

[6]

G. B. HuangQ. Y. Zhu and C. K. Siew, Extreme learning machine: Theory and applications, Neurocomputing, 70 (2006), 489-501. doi: 10.1016/j.neucom.2005.12.126. Google Scholar

[7]

S. K. ImH. M. Park and Y. W. Kim, An biometric identification system by extracting hand vein patterns, Journal-korean Physical Society, 3 (2001), 268-272. Google Scholar

[8]

M. KonoH. Ueki and S. I. Umemura, A new method for the identification of individuals by using of vein pattern matching of a finger, The Fifth Symposium on Pattern Mea-surement, (2000), 9-12. Google Scholar

[9]

A. Kumar and K. V. Prathyusha, Personal authentication using hand vein triangulation and knuckle shape, IEEE Transactions on Image processing, 18 (2009), 2127-2136. doi: 10.1109/TIP.2009.2023153. Google Scholar

[10]

X. LiS. Guo and F. Gao, Vein pattern recognitions by moment invariants, Bioinformatics and Biomedical Engineering, (2007), 612-615. doi: 10.1109/ICBBE.2007.160. Google Scholar

[11]

D. Mulyono and H. S. Jinn, A study of finger vein biometric for personal identification, International Symposium on Biometrics and Security Technologies, (2008), 1-8. doi: 10.1109/ISBAST.2008.4547655. Google Scholar

[12]

B. Y. QuB. F. Lang and J. J. Liang, Two-hidden-layer extreme learning machine for regression and classification, Neurocomputing, 175 (2016), 826-834. doi: 10.1016/j.neucom.2015.11.009. Google Scholar

[13]

N. SugandhiM. Mathankumar and V. Priya, Real time authentication system using advanced finger vein recognition technique, International Conference on Communications and Signal Processing, (2014), 1183-1187. Google Scholar

[14]

J. WangQ. Chang and J. Peng, The Optimization of the Extreme Learning Machine and fitting analysis, Journal of Zhengzhou University (Engineering Science), 37 (2016), 20-24. Google Scholar

[15]

L. Wang and G. Leedham, Near and far infrared imaging for vein pattern biometrics, Video and Signal Based Surveillance, (2006), 52-58. doi: 10.1109/AVSS.2006.80. Google Scholar

[16]

Y. WangK. Li and J. Cui, Hand-dorsa vein recognition based on partition local binary pattern, IEEE 10th International Conference on Signal Processing, (2010), 1671-1674. doi: 10.1109/ICOSP.2010.5656717. Google Scholar

[17]

K. S. WuJ. C. Lee and T. M. Lo, A secure palm vein recognition system, Journal of Systems and Software, 86 (2013), 2870-2876. doi: 10.1016/j.jss.2013.06.065. Google Scholar

[18]

J. Yang and X. Li, Efficient finger vein localization and recognition, Pattern Recognition, (2010), 1148-1151. doi: 10.1109/ICPR.2010.287. Google Scholar

[19]

W. YangX. Huang and F. Zhou, Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion, Information Sciences, 268 (2014), 20-32. doi: 10.1016/j.ins.2013.10.010. Google Scholar

show all references

References:
[1]

T. ChenJ. Cai and L. Guo, Research on hybrid face recognition algorithm based on voting Extreme Learning Machine, Journal of Zhengzhou University (Engineering Science), 37 (2016), 37-41. Google Scholar

[2]

J. M. Cross and C. L. Smith, Thermographic imaging of the subcutaneous vascular network of the back of the hand for biometric identification, Institute of Electrical and Electronics Engineers Conference on Security Technology, (1995), 20-35. doi: 10.1109/CCST.1995.524729. Google Scholar

[3]

S. DongJ. Yang and Y. Chen, Finger vein recognition based on multi-orientation weighted symmetric local graph structure, KSII Transactions on Internet and Information Systems, 9 (2015), 4126-4142. Google Scholar

[4]

J. Hashimoto, Finger vein authentication technology and its future, Symposium on VLSI Circuits, (2006), 5-8. doi: 10.1109/VLSIC.2006.1705285. Google Scholar

[5]

C. B. HsuS. S. Hao and J. C. Lee, Personal authentication through dorsal hand vein patterns, Optical Engineering, 50 (2011), 087201, 10pp. doi: 10.1117/1.3607413. Google Scholar

[6]

G. B. HuangQ. Y. Zhu and C. K. Siew, Extreme learning machine: Theory and applications, Neurocomputing, 70 (2006), 489-501. doi: 10.1016/j.neucom.2005.12.126. Google Scholar

[7]

S. K. ImH. M. Park and Y. W. Kim, An biometric identification system by extracting hand vein patterns, Journal-korean Physical Society, 3 (2001), 268-272. Google Scholar

[8]

M. KonoH. Ueki and S. I. Umemura, A new method for the identification of individuals by using of vein pattern matching of a finger, The Fifth Symposium on Pattern Mea-surement, (2000), 9-12. Google Scholar

[9]

A. Kumar and K. V. Prathyusha, Personal authentication using hand vein triangulation and knuckle shape, IEEE Transactions on Image processing, 18 (2009), 2127-2136. doi: 10.1109/TIP.2009.2023153. Google Scholar

[10]

X. LiS. Guo and F. Gao, Vein pattern recognitions by moment invariants, Bioinformatics and Biomedical Engineering, (2007), 612-615. doi: 10.1109/ICBBE.2007.160. Google Scholar

[11]

D. Mulyono and H. S. Jinn, A study of finger vein biometric for personal identification, International Symposium on Biometrics and Security Technologies, (2008), 1-8. doi: 10.1109/ISBAST.2008.4547655. Google Scholar

[12]

B. Y. QuB. F. Lang and J. J. Liang, Two-hidden-layer extreme learning machine for regression and classification, Neurocomputing, 175 (2016), 826-834. doi: 10.1016/j.neucom.2015.11.009. Google Scholar

[13]

N. SugandhiM. Mathankumar and V. Priya, Real time authentication system using advanced finger vein recognition technique, International Conference on Communications and Signal Processing, (2014), 1183-1187. Google Scholar

[14]

J. WangQ. Chang and J. Peng, The Optimization of the Extreme Learning Machine and fitting analysis, Journal of Zhengzhou University (Engineering Science), 37 (2016), 20-24. Google Scholar

[15]

L. Wang and G. Leedham, Near and far infrared imaging for vein pattern biometrics, Video and Signal Based Surveillance, (2006), 52-58. doi: 10.1109/AVSS.2006.80. Google Scholar

[16]

Y. WangK. Li and J. Cui, Hand-dorsa vein recognition based on partition local binary pattern, IEEE 10th International Conference on Signal Processing, (2010), 1671-1674. doi: 10.1109/ICOSP.2010.5656717. Google Scholar

[17]

K. S. WuJ. C. Lee and T. M. Lo, A secure palm vein recognition system, Journal of Systems and Software, 86 (2013), 2870-2876. doi: 10.1016/j.jss.2013.06.065. Google Scholar

[18]

J. Yang and X. Li, Efficient finger vein localization and recognition, Pattern Recognition, (2010), 1148-1151. doi: 10.1109/ICPR.2010.287. Google Scholar

[19]

W. YangX. Huang and F. Zhou, Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion, Information Sciences, 268 (2014), 20-32. doi: 10.1016/j.ins.2013.10.010. Google Scholar

Figure 1.  The flow chat of the wrist vein recognition system
Figure 2.  Transmitting of light
Figure 3.  The obtained wrist vein images
Figure 4.  Regions of interest
Figure 5.  Histogram comparison between original and transformed images
Figure 6.  Comparison between original and transformed images
Figure 7.  Workflow of TELM
Figure 8.  Network structure of TELM
Figure 9.  The comparison of TELM and ELM
Table 1.  The performance of TELM with and without gray normalization
TELM with gray normalization TELM without gray normalization
99.58±0.0671% 94.39±0.4271%
TELM with gray normalization TELM without gray normalization
99.58±0.0671% 94.39±0.4271%
Table 2.  Performances of different algorithms
Algorithm name Accuracy (%) Ttraining(ms) Ttesting(ms) FRR FAR
TELM99.58±0.06718.15550.01770.42%0.74%
ELM96.35±0.05952.91580.01563.65%1.57%
SVM91.40±0.05920.01090.00978.60%10.25%
NB96.21±0.10050.01390.15483.79%2.54%
Algorithm name Accuracy (%) Ttraining(ms) Ttesting(ms) FRR FAR
TELM99.58±0.06718.15550.01770.42%0.74%
ELM96.35±0.05952.91580.01563.65%1.57%
SVM91.40±0.05920.01090.00978.60%10.25%
NB96.21±0.10050.01390.15483.79%2.54%
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