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

Cai-Tong Yue; Jing Liang; Bo-Fei Lang; Bo-Yang Qu

doi:10.3934/bdia.2017008

Article Contents

2017, Volume 2, Issue 1: 59-68. Doi: 10.3934/bdia.2017008

This issue Previous Article A moving block sequence-based evolutionary algorithm for resource investment project scheduling problems Next Article Multiple-instance learning for text categorization based on semantic representation

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
* Corresponding author: liangjing@zzu.edu.cn

Published: January 2017

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

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Abstract

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.

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Mathematics Subject Classification: Primary: 68T10; Secondary: 68U10.

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Figure 1. The flow chat of the wrist vein recognition system

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Figure 2. Transmitting of light

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Figure 3. The obtained wrist vein images

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Figure 4. Regions of interest

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Figure 5. Histogram comparison between original and transformed images

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Figure 6. Comparison between original and transformed images

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Figure 7. Workflow of TELM

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Figure 8. Network structure of TELM

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Figure 9. The comparison of TELM and ELM

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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%

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Table 2. Performances of different algorithms

Algorithm name	Accuracy (%)	T_training(ms)	T_testing(ms)	FRR	FAR
TELM	99.58±0.0671	8.1555	0.0177	0.42%	0.74%
ELM	96.35±0.0595	2.9158	0.0156	3.65%	1.57%
SVM	91.40±0.0592	0.0109	0.0097	8.60%	10.25%
NB	96.21±0.1005	0.0139	0.1548	3.79%	2.54%

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