A Classification Approach to Multi-biometric Score Fusion

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3546)


The use of biometrics for identity verification of an individual is increasing in many application areas such as border/port entry/exit, access control, civil identification and network security. Multi-biometric systems use more than one biometric of an individual. These systems are known to help in reducing false match and false non-match errors compared to a single biometric device. Several algorithms have been used in literature for combining results of more than one biometric device. In this paper we discuss a novel application of random forest algorithm in combining matching scores of several biometric devices for identity verification of an individual. Application of random forest algorithm is illustrated using matching scores data on three biometric devices: fingerprint, face and hand geometry. To investigate the performance of the random forest algorithm, we conducted experiments on different subsets of the original data set. The results of all the experiments are exceptionally encouraging.


Random Forest Class Label Terminal Node Gini Index External Testing 
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.
    Jain, A., 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 (2003)Google Scholar
  2. 2.
  3. 3.
    Lee, D., Srihari, S.N.: Handprinted Digit Recognition: A Comparison of Algorithms. In: The Proceedings of the 3rd International Workship on Frontiers in Handwriting Recognition, Buffalo, NY, pp. 153–162 (1993)Google Scholar
  4. 4.
    Lam, L., Suen, C.Y.: Application of Majority Voting to Pattern Recognition: An Analysis of Its Behavior and Performance. IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans 27(5) (1997)Google Scholar
  5. 5.
    Zuev, Y., Ivanon, S.: The Voting as a Way to Increase the Decision Reliability. In: Foundations of Information/Decision Fusion with Applications to Engineering Problems, Washington, DC, pp. 206–210 (1996)Google Scholar
  6. 6.
    Tou, J.T., Gonzalez, R.C.: Pattern Recognition Principles. Addison-Wesley Publishing Co., Reading (1981)Google Scholar
  7. 7.
    Nandakumar, K., Jain, A., Ross, A.: Score Normalization in Multimodal Biometric Systems, Available at:
  8. 8.
    Xu, L., Krzyzak, A., Suen, C.Y.: Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition. IEEE Transactions on Systems, Man, and Cybernetics 22(3) (1992)Google Scholar
  9. 9.
    Verlinde, P., Chollet, G.: Comparing Decision Fusion Paradigms Using k-NN Based Classifiers, Decision Trees and Logistic Regression in a Multimodal Identity Verification Application. In: Proceedings of the 2nd International Conference on Audio and Video-Based Biometric Person Authentication (AVBPA), Washington, DC, pp. 189–193 (1999)Google Scholar
  10. 10.
    Tahani, H., Keller, J.M.: Information Fusion in Computer Vision Using the Fuzzy Integral. IEEE Transactions on Systems, Man and Cybernetics 20(3), 733–741 (1990)CrossRefGoogle Scholar
  11. 11.
    Lipnickas, A.: Classifiers Fusion with Data Dependent Aggregation Schemes. In: 7th International Conference on Information Networks. Systems and Technologies ICINASTe-2001Google Scholar
  12. 12.
    Ceccarelli, M., Petrosino, A.: Multi-feature Adaptive Classifiers for SAR Image Segmentation. Neurocomputing 14, 345–363 (1997)CrossRefGoogle Scholar
  13. 13.
    Ross, A., Jain, A.: Information Fusion in Biometrics. Pattern Recognition Letters 24, 2115–2125 (2003)CrossRefGoogle Scholar
  14. 14.
    Kittler, J., Hatef, M., Duin, R., Matas, J.: On Combining Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3) (1998)Google Scholar
  15. 15.
    Snelick, R., Indovina, M., Yen, J., Mink, A.: Multimodal Biometrics: Issues in Design and Testing. In: Proceedings of the 5th International Conference on Multimodal Interfaces, Vancouver, Canada (2003)Google Scholar
  16. 16.
    Chen, C., Liaw, A., Breiman, L.: Using Random Forest to Learn Imbalanced Data, Available at:
  17. 17.
    Remlinger, K.S.: Introduction and Application of Random Forest on High Throughput Screening Data from Drug Discovery, Available at
  18. 18.
    Breiman, L.: Random Forests. Machine Learning 45, 5–32 (2001)zbMATHCrossRefGoogle Scholar
  19. 19.
    Breiman, L., Cutler, A.: Random Forests: Classification/Clustering (2004), Available at
  20. 20.
    Breiman, L.: Wald Lecture II, Looking Inside the Black Box, Available at:
  21. 21.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth, Belmont (1984)zbMATHGoogle Scholar
  22. 22.
    Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)zbMATHMathSciNetGoogle Scholar
  23. 23.
    Liaw, A., Chen, C., Breiman, L.: Learning From Extremely Imbalanced Data With Random Forests. In: Computational Biology and Bioinformatics, 36th Symposium on the Interface, Baltimore, Maryland (2004)Google Scholar
  24. 24.
    Oh, J., Laubach, M., Luczak, A.: Estimating Neuronal Variable Importance with Random Forest. In: Proceedings of the 29th Annual Northeast Bioengineering Conference, NJIT, Newark, NJ (2003)Google Scholar
  25. 25.
    Speed, T. (ed.): Statistical Analysis of Gene Expression Microarray Data. Chapman & Hall/CRC (2003)Google Scholar
  26. 26.

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  1. 1.Department of StatisticsWest Virginia UniversityMorgantownUSA
  2. 2.Lane Department of Computer Science and Electrical EngineeringWest Virginia UniversityMorgantownUSA

Personalised recommendations