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A Competent Algorithm for Enhancing Low-Quality Finger Vein Images Using Fuzzy Theory

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Computational Intelligence in Data Mining

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 711))

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Abstract

Soft computing methods and the fuzzy theoretic approaches, in particular, are widely known for their ability to tackle the uncertainties and vagueness that exist in image processing problems. This paper puts forward a distinctive enhancement algorithm for finger vein biometric images in which interval type-2 fuzzy sets are used. Finger vein biometrics is one of the latest reliable biometric systems that make use of the uniqueness of the finger vein patterns of individuals. Low contrast, blur, or noise often result in the lower quality of the captured finger vein images. For efficient enhancement of the finger vein images, interval type-2 fuzzy set is presented in this work and Einstein T-conorm is suggested for type reduction by combining the upper and lower membership functions. The performance assessment of the proposed algorithm is done by estimating the linear index of fuzziness and entropy. The experiments are performed using different vein pattern images, and the outcomes are analyzed by comparing with the existing methods. The performance evaluation visibly exhibits the efficiency of the recommended method in comparison with the existing methods.

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References

  1. Hashimoto, J.: Finger vein authentication technology and its future. In: VLSI Circuits, Symposium on Digest of Technical Papers, pp. 5–8 (2006).

    Google Scholar 

  2. Miura, N., Nagasaka, A., Miyatake, T.: Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Machine Vision and Applications. 15(4), 194–203 (2004).

    Article  Google Scholar 

  3. Kumar, A., Zhou, Y.: Human identification using finger images. In: IEEE Transactions on image processing, pp. 2228–44 (2012).

    Article  MathSciNet  Google Scholar 

  4. Yang, J., Shi, Y., Yang, J., Jiang, L.: A novel finger-vein recognition method with feature combination. In: 16th IEEE International Conference on Image Processing, pp. 2709–2712 (2009).

    Google Scholar 

  5. Zhang, J., Yang, J.: Finger-vein image enhancement based on combination of gray-level grouping and circular gabor filter. In: International Conference on Information Engineering and Computer Science, pp. 1–4 (2009).

    Google Scholar 

  6. Kejun, W., Jingyu, L., Oluwatoyin, P.P., Weixing, F.: Finger vein identification based on 2-D gabor filter. In: 2nd International Conference on Industrial Mechatronics and Automation (ICIMA), Vol. 2, pp. 10–13 (2010).

    Google Scholar 

  7. Cho, S.R., Park, Y.H., Nam, G.P., Shin, K.Y., Lee, H.C., Park, K.R., Kim, S.M., Kim, H.C.: Enhancement of finger-vein image by vein line tracking and adaptive gabor filtering for finger-vein recognition. In: Applied Mechanics and Materials, Vol. 145, pp. 219–223 (2012).

    Article  Google Scholar 

  8. Zhang, Z., Ma, S., Han, X.: Multiscale feature extraction of finger-vein patterns based on curvelets and local interconnection structure neural network. In: 18th International Conference on Pattern Recognition, Vol. 4, pp. 145–148 (2006).

    Google Scholar 

  9. Li, H.B., Yu, C.B., Zhang, D.M., Zhou, Z.M.: The Study on Finger Vein Image Enhancement Based on Ridgelet Transformation. In: 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), pp. 1–4 (2010).

    Google Scholar 

  10. Wen, X.B., Zhao, J.W., Liang, X.Z.: Image Enhancement of Finger-vein Patterns Based on Wavelet Denoising and Histogram Template Equalization. Journal of Jilin University (Science Edition), 2, p. 026 (2008).

    Google Scholar 

  11. Pi, W., Shin, J., Park, D.: An effective quality improvement approach for low quality finger vein image. In: International Conference on Electronics and Information Engineering (ICEIE), Vol. 1, pp. V1–424 (2010).

    Google Scholar 

  12. Yu, C.B., Zhang, D.M., Li, H.B., Zhang, F.F.: Finger-vein image enhancement based on muti-threshold fuzzy algorithm. In: 2nd International Congress on Image and Signal Processing, pp. 1–3 (2009).

    Google Scholar 

  13. Shin, K.Y., Park, Y.H., Nguyen, D.T., Park, K.R.: Finger-vein image enhancement using a fuzzy-based fusion method with gabor and retinex filtering. Sensors, 14(2), pp. 3095–3129 (2014).

    Article  Google Scholar 

  14. You, L., Sun, L., Zhang, J.: Finger Vein Images Enhancement Method Based on Fuzzy Set and Oriented Filter. International Journal of Digital Content Technology and its Applications, 6(23), p. 271 (2012).

    Google Scholar 

  15. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning-I. Information sciences, 8.3, 199–249 (1975).

    Article  MathSciNet  Google Scholar 

  16. Zadeh, L.A.: Fuzzy sets, Information and control, 8.3, 338–353 (1965).

    Article  MathSciNet  Google Scholar 

  17. Mizumoto, M., Tanaka, K.: Some properties of fuzzy sets of type 2. Information and control, 31(4), pp. 312–340 (1976).

    Article  MathSciNet  Google Scholar 

  18. Nieminen, J.: On the algebraic structure of fuzzy sets of type 2. Kybernetika, 13(4), pp. 261–273 (1977).

    Google Scholar 

  19. Karnik, N.N., Mendel, J.M., Liang, Q.: Type-2 fuzzy logic systems. IEEE transactions on Fuzzy Systems, 7(6), pp. 643–658 (1999).

    Article  Google Scholar 

  20. Karnik, N.N., Mendel, J.M.: Operations on type-2 fuzzy sets. Fuzzy sets and systems, 122(2), pp. 327–348 (2001).

    Article  MathSciNet  Google Scholar 

  21. Karnik, N.N., Mendel, J.M.: Centroid of a type-2 fuzzy set. Information Sciences, 132(1), pp. 195–220 (2001).

    Article  MathSciNet  Google Scholar 

  22. Liang, Q., Mendel, J.M.: Interval type-2 fuzzy logic systems: theory and design, IEEE Transactions on Fuzzy systems, 8.5, pp. 535–550 (2000).

    Google Scholar 

  23. John, R.I., Innocent, P.R., Barnes, M.R.: Type 2 fuzzy sets and neuro-fuzzy clustering of radiographic tibia images. In: IEEE World Congress on Computational Intelligence Fuzzy Systems Proceedings, Vol. 2, pp. 1373–1376 (1998).

    Google Scholar 

  24. Yager, R.R.: Fuzzy subsets of type II in decisions. Cybernetics and System, 10(1–3), pp. 137–159 (1980).

    Article  MathSciNet  Google Scholar 

  25. Wagenknecht, M., Hartmann, K.: Application of fuzzy sets of type 2 to the solution of fuzzy equations systems. Fuzzy Sets and Systems, 25(2), pp. 183–190 (1988).

    Article  MathSciNet  Google Scholar 

  26. Tizhoosh, H.R.: Image thresholding using type II fuzzy sets. Pattern recognition, 38(12), pp. 2363–2372 (2005).

    Article  Google Scholar 

  27. Melin, P., Castillo, O.: An intelligent hybrid approach for industrial quality control combining neural networks, fuzzy logic and fractal theory. Information Sciences, 177(7), pp. 1543–1557 (2007).

    Article  Google Scholar 

  28. Chaira, T.: An improved medical image enhancement scheme using Type II fuzzy set. Applied soft computing, 25, pp. 293–308 (2014).

    Article  Google Scholar 

  29. The Hong Kong Polytechnic University finger image database (version 1.0), http://www4.comp.polyu.edu.hk/~csajaykr/fvdatabase.htm.

  30. Ensafi, P., Tizhoosh, H.: Type-2 fuzzy image enhancement. Image analysis and recognition, pp. 159–166 (2005).

    Google Scholar 

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Authors and Affiliations

  1. School of Advanced Sciences, VIT University, Vellore, 632014, Tamil Nadu, India

    Rose Bindu Joseph & Devarasan Ezhilmaran

Authors
  1. Rose Bindu Joseph
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  2. Devarasan Ezhilmaran
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Corresponding author

Correspondence to Rose Bindu Joseph .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering & Information Technology, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  2. Department of Computer Science and Engineering, Sri Sivani College of Engineering (SSCE), Srikakulam, Andhra Pradesh, India

    Janmenjoy Nayak

  3. Department of Computer Application, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Bighnaraj Naik

  4. Machine Intelligence Research (MIR) Lab, Auburn, WA, USA

    Ajith Abraham

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Joseph, R.B., Ezhilmaran, D. (2019). A Competent Algorithm for Enhancing Low-Quality Finger Vein Images Using Fuzzy Theory. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_73

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