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

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

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

    Citation:

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  • 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%
     | Show Table
    DownLoad: CSV

    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%
     | Show Table
    DownLoad: CSV
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