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Finger-vein image quality evaluation based on the representation of grayscale and binary image

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Abstract

In this paper, we propose a novel quality assessment of finger-vein images for quality control in the enrollment and authentication of a finger-vein verification system. First, a Radon transform based model is employed to assess the quality of a finger-vein grayscale image. Second, to assess the quality of a finger-vein binary image, we further proposed three evaluation functions to measure the connectivity, smoothness and reliability of the binary version of the finger-vein image. Finally, the scores from the finer-vein binary images are fused with these from finger-vein grayscale images to improve the performance. Experimental results show that our approach can effectively identify the low quality finger-vein images, which is also helpful in improving the performance of the finger-vein verification system. We also show that instead of choosing the images with the highest quality as the enrollment templates, using the templates with the mid-range quality would achieve better performance with respect to improvement of varication accuracy.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China(Grant No. 61402063), the Natural Science Foundation Project of Chongqing (Grant No. cstc2013kjrc-qnrc40013; Grant No.cstc2014jcyjA1316), and the Scientific Research Foundation of Chongqing Technology and Business University(Grant No. 1352019; Grant No.2013-56-04).

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Authors and Affiliations

  1. National research base of intelligent manufaturing service, Xue Fu Road, Chongqing, 400050, China

    Huafeng Qin

  2. School of Computer Science and Information Engineering, Chongqing Technology and Business University, Chongqing, 400067, China

    Huafeng Qin & Xiping He

  3. Chongqing University of Technology, Chongqing, 400050, China

    Ziran Chen

Authors
  1. Huafeng Qin
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  2. Ziran Chen
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  3. Xiping He
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Corresponding author

Correspondence to Huafeng Qin.

Appendix

Appendix

As we have shown in Section 4, how the templates are chosen will have an impact on the performance of a finger vein recognition system (EER is different when the templates are manually or automatically selected). In this appendix, we carry out experiments on database A to analyze the impact of different template selection strategies on the performance of a finger-vein recognition system [25]. Note that the system security level is still assumed to be 0.01% as in Section 4.

Among the 20 finger-vein images of each of the 200 fingers in the testing database established in Section 4, we choose the image with the highest, mid and lowest quality scores as the template, respectively. The following two types of scores are defined as the mid quality score in our analysis.

  1. 1)

    The 10th highest score among the scores of the 20 images.

  2. 2)

    The closest score to the average score of the 20 images. For simplicity, these two types of scores are termed as Type 1 and Type 2 mid scores.

The ROCs of the finger-vein recognition system are shown in Fig. 16 under different template selection strategies, with the corresponding EER given in Table 6. For comparison, we also report the ROC of the system when the templates are manually selected, i.e., the image with the highest quality by human vision is selected as the template. Note that we use the same protocol for computing the FRR and FAR as in Section 4. It can be seen that the mid score selection strategy achieves the lowest EER, which is around 1% lower compared with using other template selection strategies including the manual selection. For Approach A, the templates with the Type 2 mid score works the best with EER = 5.55%. For Approach B, the lowest EER is 5.95% which is achieved by using the templates with the Type 1 mid score. Both approaches show that using Type 1 or Type 2 mid score selection strategy can achieve a lower EER than using other strategies (see Table 6). Thus, one should select the image with the mid-range quality as the template enrolled in the database. This may be explained by following fact. In general, the high quality finger-vein image contains more vein feature than middle quality image. Compared to match two middle quality images, matching high quality finger-vein mage with middle quality image may create more errors. Moreover, most of middle quality finger-vein images instead of high quality finger-vein image may be captured in a practical finger-vein capturing system. Therefore, the ERR increases when the finger-vein image with high quality is selected as template.

Fig. 16
figure 16

ROC with respect to various templates selected by (a) gray image based method and (b) its extension version

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Table 6 The EER (%) of the finger-vein recognition system under different template selection strategies
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Qin, H., Chen, Z. & He, X. Finger-vein image quality evaluation based on the representation of grayscale and binary image. Multimed Tools Appl 77, 2505–2527 (2018). https://doi.org/10.1007/s11042-016-4317-y

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