A palm vein identification system based on Gabor wavelet features


As a new and promising biometric feature, thermal palm vein pattern has drawn lots of attention in research and application areas. Many algorithms have been proposed for authentication since palm vein has special characteristics, such as liveness detection and hard to forgery. However, the detection accuracy of palm vein quite depends on the preprocessing and feature representation, which is supposed to be translation and rotation invariant to some extent. In this paper, we proposed an effective method for palm vein identification based on Gabor wavelet features which contains five steps: image acquisition, ROI detection, image preprocessing, features extraction, and matching. The 178 palm vein images from 101 persons were used to test the proposed palm vein recognition approach, where 176 images were correctly recognized with two in failure. The experimental results demonstrate the effectiveness of the proposed approach.

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  1. 1.

    Yangqiang Z, Dongmei S, Zhengding Q (2012) Hand-based single sample biometrics recognition. Neural Comput Appl 21(8):1835–1844

    Article  Google Scholar 

  2. 2.

    Soni M, Gupta S, Rao MS, Gupta P (2010) A new vein pattern-based verification system. Int J Comput Sci Inform Secur 8(1):58–63

    Google Scholar 

  3. 3.

    Doublet J, Lepetit O, Revenu M (2007) Contactless palmprint authentication using circular gabor filter and approximated string matching. In: Proceedings of international conference on signal and image processing (SIP), Honolulu, pp 511–516

  4. 4.

    Wang L, Leedham G (2005) A thermal hand vein pattern verification system. In: Proceedings of international conference on pattern recognition and image analysis, LNCS 3687, pp 58–65

  5. 5.

    Chen H-F, Lu G-M, Wang R (2009) A new palm vein matching method based on ICP algorithm. In: Proceedings of international conference on interaction sciences: information technology, culture and human, Seoul, Korea, pp 1207–1211

  6. 6.

    Li X-Y, Guo S-X, Gao F-L, Li Y (2007) Vein pattern recognitions by moment invariants. In: Proceedings of international conference of bioinformatics and biomedical engineering, Wuhan, China, pp 612–615

  7. 7.

    Yang JC, Xie SJ, Yoon S, Park DS, Fang ZJ, Yang SY (2013) Fingerprint matching based on extreme learning machine. Neural Comput Appl 22(3–4):435–445

    Article  Google Scholar 

  8. 8.

    Fan KC, Lin CL, Lee WL (2004) Biometric verification using thermal images of palm-dorsa vein patterns. IEEE Trans Circuits Syst Video Technol 14(2):199–213

    Article  Google Scholar 

  9. 9.

    Wang LY, Leedham G, Cho DS (2008) Minutiae feature analysis for infrared hand vein pattern biometrics. Pattern Recogn 41(3):450–453

    Article  Google Scholar 

  10. 10.

    Zhang YB, Li Q, Yan J, Bhattacharya P (2007) Palm vein extraction and matching for personal authentication. In: Proceeding of international conference on advances in visual information systems, LNCS 4781, pp 154–164

  11. 11.

    Hsu CB, Hao SS, Lee JC (2011) Personal authentication through dorsal hand vein patterns. Opt Eng 50(8):087201.1–087201.10

    Google Scholar 

  12. 12.

    Wang JG, Yau WY, Suwandy A, Sung E (2008) Person recognition by fusing palmprint and palm vein images based on “Laplacianpalm” representation. Pattern Recogn 41(5):1514–1527

    Article  MATH  Google Scholar 

  13. 13.

    Lee JC (2012) A novel biometric system based on palm vein image. Pattern Recogn Lett 33(12):1520–1528

    Article  Google Scholar 

  14. 14.

    Zhang B, Qiao Y (2010) Face recognition based on gradient gabor feature and efficient kernel fisher analysis. Neural Comput Appl 19(4):617–623

    Article  MathSciNet  Google Scholar 

  15. 15.

    Liu X, Liu JB (2008) Mammary image enhancement based on contrast limited adaptive histogram equalization. Comput Eng Appl 44(10):173–175

    Google Scholar 

  16. 16.

    Wang KJ, Ding YH, Zhuang DY, Wang DZ (2005) Threshold segmentation for hand vein image. Tech Autom Appl 24(8):19–22

    Google Scholar 

  17. 17.

    Gonzalez RC, Woods RE Digital image processing. Published by arrangement with the original publisher, Pearson Education, Inc., publishing as Prentice Hall, ISBN: 0201180758

  18. 18.

    Suleyman M, Yu FQ, Lambert S (2006) Vein feature extraction using DT-CNNs. Int workshop on cellular neural networks and their applications 1–6

  19. 19.

    Li Y, Wu GF, Dai GL, Li JJ (2010) A new algorithm to extract contour feature points of palmprint. Microelectron Comput 27(5):90–94

    Google Scholar 

  20. 20.

    Shen LL, Bai L (2006) A review of Gabor wavelets for face recognition. Pattern Anal Appl 9(2–3):273–292

    Article  MathSciNet  Google Scholar 

  21. 21.

    Li JB, Pan JS, Lu ZM (2009) Face recognition using Gabor-based complete kernel fisher discriminant analysis with fractional power polynomial models. Neural Comput Appl 18(6):613–621

    Article  Google Scholar 

  22. 22.

    Arivazhagan S, Ganesan L, Priyal SP (2006) Texture classification using Gabor wavelets based rotation invariant features. Pattern Recogn Lett 27(16):1976–1982

    Article  Google Scholar 

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This paper is supported by the Nature Science Fund of China for Young Scholars (No. 40801164), the provincial Ministry of combination of production teaching and research project funding (No. 2011B090400420) and National Key Laboratory of Science and Technology on Aerospace Intelligence Control.

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Correspondence to Zhong Chen.

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Wang, R., Wang, G., Chen, Z. et al. A palm vein identification system based on Gabor wavelet features. Neural Comput & Applic 24, 161–168 (2014). https://doi.org/10.1007/s00521-013-1514-8

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  • Palm vein
  • Biological identification
  • Gabor wavelet