Finger-Knuckle-Print Recognition Using Local Orientation Feature Based on Steerable Filter

  • Zichao Li
  • Kuanquan Wang
  • Wangmeng Zuo
Part of the Communications in Computer and Information Science book series (CCIS, volume 304)


Automatic personal identification based on finger-knuckle-print (FKP) has been considered as a promising technology in biometrics family in recent years. Previous work indicates that local orientation analysis supplies an efficient framework for FKP representation. In this paper, we propose a novel FKP recognition method using the Adaptive Steerable Orientation Coding (ASOC). High order steerable filters are first employed to extract the continuous orientation feature map, then we use multilevel histogram thresholding method to quantize the feature map adaptively and the discrete orientations are used for coding a FKP image. Furthermore, we measure the similarity between two coded FKP images by designing an effective angular matching function. Experimental results on the PolyU FKP database demonstrate the accuracy of the proposed method.


Biometrics finger-knuckle-print steerable filter local orientation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Woodard, D., Flynn, P.: Finger Surface as a Biometric Identifier. Computer Vision and Image Understanding 100(3), 357–384 (2005)CrossRefGoogle Scholar
  2. 2.
    Zhang, L., Zhang, L., Zhang, D., Zhu, H.: Online Finger-knuckle-print Verification for Personal Authentication. Pattern Recognition 43(7), 2560–2571 (2010)zbMATHCrossRefGoogle Scholar
  3. 3.
    Zhang, L., Zhang, L., Zhang, D., Zhu, H.: Ensemble of Local and Global Information for Finger-knuckle-print Recognition. Pattern Recognition 44(9), 1990–1998 (2010)CrossRefGoogle Scholar
  4. 4.
    Zhang, L., Zhang, L., Zhang, D., Guo, Z.: Phase Congruency Induced Local Features for Finger-knuckle-print Recognition. Pattern Recognition 45(1), 2522–2531 (2012)CrossRefGoogle Scholar
  5. 5.
    Yue, F., Zuo, W., Zhang, D.: ICP Registration using Principal Line and Orientation Features for Palmprint Alignment. In: Proc. ICIP, pp. 3069–3072 (2010)Google Scholar
  6. 6.
    Zhang, L.: PolyU Finger-knuckle-print Database,
  7. 7.
    Freeman, W., Adelson, E.: The Design and Use of Steerable Filters. IEEE Trans. Pattern Anal. and Mach. Intell. 13(9), 891–906 (1991)CrossRefGoogle Scholar
  8. 8.
    Jacob, M., Unser, M.: Design of Steerable Filters for Feature Detection Using Canny Like Criteria. IEEE Trans. Pattern Anal. and Mach. Intell. 26(8), 1007–1019 (2004)CrossRefGoogle Scholar
  9. 9.
    Kong, W., Zhang, D.: Competitive Coding Scheme for Palmprint Verification. In: Proc. ICIP (2004)Google Scholar
  10. 10.
    Luessi, M., Eichmann, M., Schuster, G., Katsaggelos, A.: Framework for Efficient Optimal Multilevel Image Thresholding. Journal of Electronic Imaging 18 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zichao Li
    • 1
  • Kuanquan Wang
    • 1
  • Wangmeng Zuo
    • 1
  1. 1.Biocomputing Research Center, School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina

Personalised recommendations