Biometric Recognition Using Feature Selection and Combination

  • Ajay Kumar
  • David Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3546)


Most of the prior work in biometric literature has only emphasized on the issue of feature extraction and classification. However, the critical issue of examining the usefulness of extracted biometric features has been largely ignored. Feature evaluation/selection helps to identify and remove much of the irrelevant and redundant features. The small dimension of feature set reduces the hypothesis space, which is critical for the success of online implementation in personal recognition. This paper focuses on the issue of feature subset selection and its effectiveness in a typical bimodal biometric system. The feature level fusion has not received adequate attention in the literature and therefore the performance improvement in feature level fusion using feature subset selection is also investigated. Our experimental results demonstrate that while majority of biometric features are useful in predicting the subjects identity, only a small subset of these features are necessary in practice for building an accurate model for identification. The comparison and combination of features extracted from hand images is evaluated on the diverse classification schemes; naive Bayes (normal, estimated, multinomial), decision trees (C4.5, LMT), k-NN, SVM, and FFN.


Feature Selection Support Vector Machine Classifier Feature Subset Feature Subset Selection Biometric Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Liu, C., Wechsler, H.: Independent component analysis of Gabor features for face recognition. IEEE Trans. Neural Networks 14, 919–928 (2003)CrossRefGoogle Scholar
  2. 2.
    Zhang, D., Kong, W.K., You, J., Wong, M.: On-line palmprint identification. IEEE Trans. Patt. Anal. Machine Intell. 25, 1041–1050 (2003)CrossRefGoogle Scholar
  3. 3.
    Sanchez-Reillo, R., Sanchez-Avila, C., Gonzales-Marcos, A.: Biometric identification through hand geometry measurements. IEEE Trans. Patt. Anal. Machine Intell. 22, 1168–1171 (2000)CrossRefGoogle Scholar
  4. 4.
    Ross, A., Jain, A.K.: Information fusion in Biometrics. Pattern Recognition Lett. 24, 2115–2125 (2003)CrossRefGoogle Scholar
  5. 5.
    Langley, P., Sage, S.: Scaling to domains with irrelevant features. In: Greiner, R. (ed.) Computational Learning Theory and Neural Learning Systems, vol. 4, MIT Press, Cambridge (1994)Google Scholar
  6. 6.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance based learning algorithms. Machine Learning 6, 37–66 (1991)Google Scholar
  7. 7.
    Langley, P., Sage, S.: Induction of selective Bayesian classifiers. In: Proc. 10th Intl. Conf. Uncertainty in Artificial Intelligence, Seattle, W. A, Morgan Kaufmann, San Francisco (1994)Google Scholar
  8. 8.
    Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann, Los Altos, Los Altos (1993)Google Scholar
  9. 9.
    Jain, A.K., Ross, A., Prabhakar, S.: ” An Introduction to Biometric Recognition". IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on Image- and Video-Based Biometrics 14(1), 4–20 (2004)Google Scholar
  10. 10.
    Hall, M.A., Smith, L.A.: Practical feature subset selection for machine learning. In: Proc. 21st Australian Computer Science Conference, pp. 181–191. Springer, Heidelberg (1998)Google Scholar
  11. 11.
    Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proc. 7th Intl. Conf. Machine Learning, Morgan Kaufmann Publishers, Stanford University (2000)Google Scholar
  12. 12.
    Kohavi, R., John, G., Long, R., Manley, D., Pfleger, K.: MLC++: A machine learning library in C++, available on,
  13. 13.
    Oden, C., Ercil, A., Buke, B.: Combining implicit polynomials and geometric features for hand recognition. Pattern Recognition Letters 24, 2145–2152 (2003)CrossRefGoogle Scholar
  14. 14.
    John, G.H., Langley, P.: Estimating continious distribution in Bayesian classifiers. In: Proc. 11th Conf. on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, San Mateo (1995)Google Scholar
  15. 15.
    Eyheramendy, S., Lewis, D., Madigan, D.: On the naive Bayes model for text classification. To appear in Artificial Intelligence & Statistics (2003)Google Scholar
  16. 16.
    McCallum, A., Nigam, K.: A comparison of event model for naive Bayes Text Classification. In: Proc. AAAI 1998 Workshop on Learning for Text Categorization (1998)Google Scholar
  17. 17.
    Aha, D.W., Kibler, D., Albert, K.: Instance based learning algorithms. Machine Learning 6, 37–66 (1991)Google Scholar
  18. 18.
    Vapnik, V.: Statistical Learning Theory. Wiley & Sons, Inc., New York (1998)zbMATHGoogle Scholar
  19. 19.
    Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: The PROP algorithm. In: Proc. Intl. Conf. Neural Networks, vol. 1, pp. 586–591 (1993)Google Scholar
  20. 20.
    Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  21. 21.
    Landwehr, N., Hall, M., Frank, E.: Logistic Model Trees. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) ECML 2003. LNCS (LNAI), vol. 2837, pp. 241–252. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  22. 22.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. The Annals of Statistic 38(2), 337–374 (2000)CrossRefMathSciNetGoogle Scholar
  23. 23.
    Kumar, A., Wong, D.C.M., Shen, H., Jain, A.K.: Personal verification using palmprint and hand geometry biometric. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 668–675. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  24. 24.
    Kumar, A., Zhang, D.: Integrating shape and texture for hand verification. In: Proc. ICIG 2004, Hong Kong, December 2004, pp. 326–329 (2004)Google Scholar
  25. 25.
    John, C.: Russ, The Image Processing Handbook, 3rd edn. CRC Press, Boca Eaton (1999)Google Scholar
  26. 26.
    Cristianini, N., S-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2001)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ajay Kumar
    • 1
    • 2
  • David Zhang
    • 2
  1. 1.Department of Electrical EngineeringIndian Institute of Technology DelhiHauz Khas, New DelhiIndia
  2. 2.Depertment of ComputingHong Kong Polytechnic UniversityHong Kong

Personalised recommendations