Advertisement

Image Enhancement of Finger Vein Patterns Based on the Guided Filter

  • Tao Zhan
  • Hui MaEmail author
  • Na Hu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

To solve the problem that image enhancement of finger vein patterns based on traditional filtering methods fails to intuitively highlight the feature of edge protection, the experimental study model based on the guided filter is proposed. Through adding the comparison experiment between guided filter and bilateral filter, and doing the binary processing to the finger vein image after the process of the guided filtering and bilateral filtering, it can be found that some noises exist around the vein texture. In order to reduce or eliminate the interference, a traditional average filtering method is applied for denoising, which not only highlights the vein texture details but eliminates the interference in the post-processing, and at the same time, adjusting the filter parameters will cause a significant impact on the enhancement of finger vein image. A comparison experiment in false recognition rate between two filtering algorithms is conducted, and visual and numerical evaluations are performed on finger vein image after the process of enhancement and binarization; the result indicates that the guided filter has better edge protection feature and lower false recognition rate than the bilateral filter.

Keywords

Processing Guided filter Finger vein image enhancement Bilateral filter Binary processing Median filter 

References

  1. 1.
    Cao W, Wang HB, Shi J, Yu R, Tao L. Enhancement algorithm of finger vein image based on weighted guided filter with edge detection. Laser Optoelectron Prog. 2017;54(02):172–80.Google Scholar
  2. 2.
    Qiu JH, Xu W, Wang YF. Study of finger vein recognition application. Study Inf Secur. 2016;2(01):86–92.Google Scholar
  3. 3.
    You L, Li H, Wang JW. Finger-vein recognition algorithm based on potential energy theory. In: Proceedings of the 2015 IEEE 16th international conference on industrial technology; 2016. p. 742–5.Google Scholar
  4. 4.
    Yang WM, Qin C, Wang XJ et al. Cross section binary coding for fusion of finger vein and finger dorsal texture. In: Proceedings of 2016 IEEE international conference on industrial technology; 2016. p. 742–5.Google Scholar
  5. 5.
    Wu K, Han GL, Wang YQ, Wu XT. Multi-scale guided filter and its application in image dehazing. Opt Precis Eng. 2017;25:2182–94.CrossRefGoogle Scholar
  6. 6.
    Kou F, Chen W, Li Z, et al. Content adaptive image detail enhancement. IEEE Sig Process Lett. 2015;22(2):211–5.CrossRefGoogle Scholar
  7. 7.
    Liu W, Cui YF, Wu XL. Image defogging algorithm based on constrained evolutionary time-frequency weighted filtering. Comput Sci. 2014;41(09):311–4.Google Scholar
  8. 8.
    He KM, Sun J, Tang XO. Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell. 2009;33(12):2341–53.Google Scholar
  9. 9.
    He KM, Sun J, Tang XO. Guided image filtering. IEEE Trans Pattern Anal Mach Intell. 2013;35(6):1397–409.CrossRefGoogle Scholar
  10. 10.
    Tomasi C, Manduchi R. Bilateral filtering for gray and color images. In: Proceedings of the 6th international conference on computer vision; 1998. p. 839–46.Google Scholar
  11. 11.
    Kapik B, Yannoulis NC, Shams N, Unoprostone Monotherapy Study Group—eu. Fast “0(1)” bilateral filtering. Miami: CVPR 2009 IEEE conference in computer vision and pattern recognition, 2009.Google Scholar
  12. 12.
    Sun L et al. Adaptive bilateral filter considering local characteristics. In: Hefei: 2011 six international conference in image and graphics (ICIG); 2011. p. 187–92.Google Scholar
  13. 13.
    Ye C, Yong JZ, Kamen Lvanov et al. Pre-processing for muscle motion analysis: adaptive guided image filtering for speckle reduction of ultrasound images. In: Osaka Japan: 35th annual international conference of the IEEE EMBS; 2011. p. 4026–4029.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.College of Electronic EngineeringHeilongjiang UniversityHarbinChina

Personalised recommendations