A Hough Transform Based Feature Extraction Algorithm for Finger Knuckle Biometric Recognition System

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)


Finger Knuckle Print is an emerging biometric trait to recognize one’s identity. In this paper, we have developed a novel method for finger knuckle feature extraction and its representation using Hough transform. Hough transform plays a significant role in locating features like lines, curves etc., present in the digital images by quantifying its collinear points. This paper formulates the Elliptical Hough Transform for feature extraction from the captured digital image of finger knuckle print (FKP). The primary pixel points present in the texture patterns of FKP are transformed into a five dimensional parametric space defined by the parametric representation in order to describe ellipse. The discrete coordinate of the five dimensional coordinate spaces along with its rotational angle are determined and characterized as the parameters of elliptical representation. These parametric representations are the unique feature information obtained from the captured FKP images. Further, this feature information can be used for matching various FKP images in order to identify the individuals. Extensive experimental analysis was carried out to evaluate the performance of the proposed system in terms of accuracy. The obtained results shows the lowest error rate of EER = 0.78%, which is found to be remarkable when compared to the results of existing systems presented in the literature.


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  1. 1.
    Hand-based Biometrics. Biometric Technology Today 11(7), 9–11 (2000)Google Scholar
  2. 2.
    Kumar, A., Zhang, D.: Combining fingerprint, palm print and hand shape for user authentication. In: Proceedings of International Conference on Pattern Recognition, pp. 549–552 (2006)Google Scholar
  3. 3.
    Rowe, R.K., Uludag, U., Demirkus, M., Parthasaradhi, S., Jain, A.K.: A multispectral whole-hand biometric authentication system. In: Biometric Symposium (2007)Google Scholar
  4. 4.
    Kumar, A., Ravikanth, C.: Personal Authentication Using Finger Knuckle Surface. IEEE Transactions on Information Forensics and Security 4(1), 98–110 (2009)CrossRefGoogle Scholar
  5. 5.
    Zhang, L., Zhang, L., Zhang, D.: Finger-Knuckle-Print Verification Based on Band-Limited Phase-Only Correlation. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 141–148. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Shen, L., Bai, L., Zhen, J.: Hand-based biometrics fusing palm print and finger-knuckle-print. In: 2010 International Workshop on Emerging Techniques and Challenges for Hand-Based Biometrics (2010)Google Scholar
  7. 7.
    Jing, X., Li, W., Lan, C., Yao, Y., Cheng, X., Han, L.: Orthogonal Complex Locality Preserving Projections Based on Image Space Metric for Finger-Knuckle-Print Recognition. In: 2011 International Conference on Hand-based Biometrics (ICHB), pp. 1–6 (2011)Google Scholar
  8. 8.
    Zhang, L., Li, H., Shen, Y.: A Novel Riesz Transforms based Coding Scheme for Finger-Knuckle-Print Recognition. In: 2011 International Conference on Hand-based Biometrics (2011)Google Scholar
  9. 9.
    Meraoumia, A., Chitroub, S., Bouridane, A.: Fusion of Finger-Knuckle-Print and Palmprint for an Efficient Multi-biometric System of Person Recognition. In: 2011 IEEE International Conference on Communications (2011)Google Scholar
  10. 10.
    Bao, P., Zhang, L., Wu, X.: Canny Edge Detection Enhancement by Scale Multiplication. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(9), 1485–1490 (2005)CrossRefGoogle Scholar
  11. 11.
    Zhanga, L., Zhanga, L., Zhanga, D., Zhub, H.: Online Finger-Knuckle-Print Verification for Personal Authentication, vol. 1, pp. 67–78 (2009)Google Scholar
  12. 12.
    Olson, C.F.: Constrained Hough Transforms for Curve Detection. Computer Vision and Image Understanding 73(3), 329–345 (1999)CrossRefMATHGoogle Scholar
  13. 13.
    Shen, D., Lu, Z.: Computation of Correlation Coefficient and Its Confidence Interval in SAS, vol. 31, pp. 170–131 (2005)Google Scholar
  14. 14.
    Hanmandlu, M., Grover, J., Krishanaadsu, V., Vasirkala, S.: Score level fusion of hand based Biometrics using T-Norms. In: 2010 IEEE International Conference on Technologies for Homeland Security, pp. 70–76 (2010)Google Scholar
  15. 15.
    Alen, J.V., Novak, M.: Curve Drawing Algorithms for Raster displays. ACM Transactions on Graphic 4(2), 147–169 (1985)CrossRefGoogle Scholar

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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringPondicherry Engineeing CollegePuducherryIndia

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