Abstract
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.
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References
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)
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)
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)
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)
Tou, J.T., Gonzalez, R.C.: Pattern Recognition Principles. Addison-Wesley Publishing Co., Reading (1981)
Nandakumar, K., Jain, A., Ross, A.: Score Normalization in Multimodal Biometric Systems, Available at: http://biometrics.cse.mse.edu
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)
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)
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)
Lipnickas, A.: Classifiers Fusion with Data Dependent Aggregation Schemes. In: 7th International Conference on Information Networks. Systems and Technologies ICINASTe-2001
Ceccarelli, M., Petrosino, A.: Multi-feature Adaptive Classifiers for SAR Image Segmentation. Neurocomputing 14, 345–363 (1997)
Ross, A., Jain, A.: Information Fusion in Biometrics. Pattern Recognition Letters 24, 2115–2125 (2003)
Kittler, J., Hatef, M., Duin, R., Matas, J.: On Combining Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3) (1998)
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)
Chen, C., Liaw, A., Breiman, L.: Using Random Forest to Learn Imbalanced Data, Available at: http://stat-www.berkeley.edu/users/chenchao/666.pdf
Remlinger, K.S.: Introduction and Application of Random Forest on High Throughput Screening Data from Drug Discovery, Available at http://www4.ncsu.edu/~ksremlin
Breiman, L.: Random Forests. Machine Learning 45, 5–32 (2001)
Breiman, L., Cutler, A.: Random Forests: Classification/Clustering (2004), Available at http://www.stat.berkeley.edu/users/breiman/RandomForests
Breiman, L.: Wald Lecture II, Looking Inside the Black Box, Available at: http://www.stat.berkeley.edu/users/breiman
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth, Belmont (1984)
Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)
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)
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)
Speed, T. (ed.): Statistical Analysis of Gene Expression Microarray Data. Chapman & Hall/CRC (2003)
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Ma, Y., Cukic, B., Singh, H. (2005). A Classification Approach to Multi-biometric Score Fusion. In: Kanade, T., Jain, A., Ratha, N.K. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2005. Lecture Notes in Computer Science, vol 3546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527923_50
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DOI: https://doi.org/10.1007/11527923_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27887-0
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