Home   >   CSC-OpenAccess Library   >    Manuscript Information
QPLC: A Novel Multimodal Biometric Score Fusion Method
Jayanta Basak, Kiran Kate, Vivek Tyagi, Nalini Ratha
Pages - 123 - 134     |    Revised - 15-09-2012     |    Published - 24-10-2012
Volume - 6   Issue - 5    |    Publication Date - October 2012  Table of Contents
MORE INFORMATION
KEYWORDS
Multimodal Biometrics, Normalization, Quantile Transformation, SVM
ABSTRACT
In biometrics authentication systems, it has been shown that fusion of more than one modality (e.g., face and finger) and fusion of more than one classifier (two different algorithms) can improve the system performance. Often a score level fusion is adopted as this approach doesn’t require the vendors to reveal much about their algorithms and features. Many score level transformations have been proposed in the literature to normalize the scores which enable fusion of more than one classifier. In this paper, we propose a novel score level transformation technique that helps in fusion of multiple classifiers. The method is based on two components: quantile transform of the genuine and impostor score distributions and a power transform which further changes the score distribution to help linear classification. After the scores are normalized using the novel quantile power transform, several linear classifiers are proposed to fuse the scores of multiple classifiers. Using the NIST BSSR-1 dataset, we have shown that the results obtained by the proposed method far exceed the results published so far in the literature.
CITED BY (8)  
1 Gupta, U., Sinha, P., & Chharia, P. (2015, January). Reduction of genuine and imposter score overlapping based on intra-class variations and deviation of scores in a multimodal biometrie system. In Signal Processing And Communication Engineering Systems (SPACES), 2015 International Conference on (pp. 126-131). IEEE.
2 Rathi, D., Rathi, A., & kumar, a. international journal of engineering sciences & research technology System Security by Measuring and Analyzing Biological Data Authentication Modalities.
3 Sudhamani, M. J., Venkatesha, M. K., & Radhika, K. R. (2014, December). Fusion at decision level in multimodal biometric authentication system using Iris and Finger Vein with novel feature extraction. In India Conference (INDICON), 2014 Annual IEEE (pp. 1-6). IEEE.
4 Viswanathan, R. (2014). Data Fusion. In Computer Vision (pp. 166-168). Springer US.
5 Trewin, S., Swart, C., Koved, L., Martino, J., Singh, K., & Ben-David, S. (2012, December). Biometric authentication on a mobile device: a study of user effort, error and task disruption. In Proceedings of the 28th Annual Computer Security Applications Conference (pp. 159-168). ACM.
6 Gupta, U., Fukane, J., Ramanan, V., & Thakur, R. (2012, October). Score level fusion of face and finger traits in multimodal biometric authentication system. In IJCA Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET 2012) (No. 4). Foundation of Computer Science (FCS).
7 Makihara, Y., Muramatsu, D., Yagi, Y., & Hossain, M. A. (2011, October). Score-level fusion based on the direct estimation of the Bayes error gradient distribution. In Biometrics (IJCB), 2011 International Joint Conference on (pp. 1-8). IEEE.
8 Makihara Yasushi, & Yagi Yasushi. (2011). ROC curve optimization by adaptive threshold control that is based on the reliability. IEICE D, 94 (8), 1227-1239.
1 Google Scholar 
2 CiteSeerX 
3 Scribd 
4 SlideShare 
5 PdfSR 
A. K. Jain, A. Ross, Multibiometric systems, Communications of the ACM, Special Issue on Multimodal Interfaces 47 (1), 34–40, 2004.
A. K. Jain, K. Nandakumar, and A. Ross, Score Normalization in Multimodal Biometric Systems, Pattern Recognition, vol. 38, no. 12, pp. 2270–2285, December 2005.
Andrade, C. and von Solms, S. H., Investigating and comparing multimodal biometric techniques, Policies and Research in Identity Management, IFIP International Federation for Information Processing, vol. 261, pp. 79–90, 2008.
B. Ulery, A. R. Hicklin, C. Watson, W. Fellner, and P. Hallinan, Studies of Biometric Fusion, NIST, Tech. Rep. IR 7346, September 2006.
C. Cortes and V. Vapnik, Support-vector networks, Machine learning, 20(3):273-297, 1995.
C. Dwork, R. Kumar, M. Naor and D. Sivakumar, Rank aggregation methods for the Web, WWW '01: Proceedings of the 10th international conference on World Wide Web, 613-622,2001.
Chih-Chung Chang and Chih-Jen Lin, LIBSVM: a library for support vector machines, 2001.Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
J. Kittler, M. Hatef, R. P. Duin, J. G. Matas, On Combining Classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (3), 226–239, 1998.
K. Nandakumar, Y. Chen, S. C. Dass, and A. K. Jain, Likelihood ratio based biometric score fusion, IEEE Trans on Pattern Analysis and Machine Intelligence, Vol. 30, No. 2, Feb 2008.
M. C. Zoepfl and H. J. Korves, Improving identity discovery through fusion, ITPro Jan/Feb 2009.
M. L. Gavrilova and M. M. Monwar, Fusing multiple matcher’s outputs for secure human identification, Int. J. Biometrics, 1(3), 329-348, 2009.
M. Vatsa, R. Singh, A. Noore, and M. Houck, Quality-augmented fusion of level-2 and level-3 fingerprint information using DSm theory, International Journal of Approximate Reasoning,50(1), 2009.
M. Vatsa, R. Singh, A. Ross, and A. Noore, Likelihood ratio in a SVM framework: fusing linear and non-linear face classifiers, IEEE Computer Vision and Pattern Recognition Workshop, Anchorage, AK, 1-6, 2008.
M. Vatsa, R. Singh, and A. Noore, Improving Iris Recognition Performance using Segmentation, Quality Enhancement, Match Score Fusion and Indexing, IEEE Transactions on Systems, Man, and Cybernetics - B, 38(3), 2008.
M. Villegas and R. Paredes, Score Fusion by Maximizing the Area under the ROC Curve,Pattern Recognition and Image Analysis: 4th Iberian Conference, IbPRIA, June 2009.
N. Poh, J.V. Kittler, and T. Bourlai, Quality-based score normalization with device qualitative information for multimodal biometric fusion, IEEE Transactions Systems, Man, and Cybernetics – Part A, 40(3), 539-554, 2010.
National Institute of Standards and Technology, NIST Biometric Scores Set – Release 1,http://www.itl.nist.gov/iad/894.03/biometricscores/, 2004.
R. Singh, M. Vatsa, and A. Noore, “Intelligent Biometric Information Fusion using Support Vector Machine,” Soft Computing in Image Processing: Recent Advances, M. Nachtegael, D.Weken, E. Kerre, and W. Philips (editors), Springer-Verlag Publishers, 2008.
S. Garcia-Salicetti, M.A. Mellakh, L. Allano, and B. Dorizzi, Multimodal biometric score fusion:the mean rule vs. support vector classifiers, Proc. EUSIPCO, 2005.
S. Prabhakar, A. K. Jain, Decision-level Fusion in Fingerprint Verification, Pattern Recognition 35 (4), 861–874, 2002.
Dr. Jayanta Basak
NetApp - India
basakjayanta@yahoo.com
Miss Kiran Kate
IBM Research - Singapore
Mr. Vivek Tyagi
IBM Research - India
Miss Nalini Ratha
IBM T J Watson Research Center Hawthorne - United States of America


CREATE AUTHOR ACCOUNT
 
LAUNCH YOUR SPECIAL ISSUE
View all special issues >>
 
PUBLICATION VIDEOS