A Likelihood Ratio Approach to Information Fusion for Image-Based Fingerprint Verification
Subject Areas : electrical and computer engineeringM. S. Helfroush 1 * , M. Mohammadpour 2
1 -
2 -
Keywords: Fingerprint Training-based fusion Image-based features Likelihood ratio Parzen density estimation,
Abstract :
Image-based fingerprint verification systems have been considered as a parallel method against the minutiae-based approach. This paper proposes a training based fusion method for fingerprint verification, using likelihood ratio (L.R). In this method, the matching scores which are extracted from orientation, spectral and textural features are fused. In order to fuse these image-based features, the likelihood ratio approach has been employed. FVC2000 database has been selected to evaluate the method. Also, the proposed method has been compared to a similar one that uses the simple sum as its fusion system. The comparison results show that the proposed fusion method has made a significant improvement for the accuracy of matching system, so that the equal error rate (ERR) of proposed system has been reduced to 0.14%.
[1] D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Hand Book of Fingerprint Recognition, Springer, New York, 2003.
[2] M. S. Helfroush and H. Ghassemian, "Nonminutiae - based decision - level fusion for fingerprint verification," EURASIP J. on Advances in Signal Processing, vol. 2700, no. 1, pp. 1-11, 2007.
[3] J. Kour and N. Awasthi, "Nonminutiae based fingerprint matching," in Proc. Int. Association of Computer Science and Information Technology - Spring Conf., IACSITSC'09, pp. 199-203, Apr. 2009.
[4] L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms, John Wiley & Sons, New Jersey, 2004.
[5] J. Kittler, M. Hatef, R. P. W. Duin, and J. Matas, "On combining classifiers," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 226-239, Mar. 2000.
[6] M. Vatsa, R. Singh, and A. Noore, "Unification of evidence-theoretic fusion algorithms: a case study in level - 2 and level - 3 fingerprint features," IEEE Trans. on Systems, Man and Cybernetics, pt. A: Systems and Humans, vol. 39, no. 1, pp. 47-56, Jan. 2009.
[7] A. Jain, R. P. W. Duin, and J. Mao, "Statistical pattern recognition: a review," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 226-239, Jan. 2000.
[8] S. Prabhakar and A. Jain, "Decision level fusion in fingerprint verification," Pattern Recognition, vol. 35, no. 4, pp. 861-874, Apr. 2002.
[9] A. M. Bazen, N. J. Veldhuis, "Likelihood-ratio-based biometric verification," IEEE Trans. on Circuits for Video Technology, vol. 14, no. 1, Jan. 2004.
[10] K. Kryszczuk and A. Drygajlo, "Improving biometric verification with class - independent quality information," Signal Processing, IET, vol. 3, no. 4, pp. 310-321, Jul. 2009.
[11] H. L. Van Trees, Detection, Estimation, and Modulation Theory, John Wiley & Sons, New York, 1968.
[12] M. Girolami and C. He, "Probability density estimation from optimally condensed data samples," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1253-1264, Oct. 2003.
[13] A. Elgammal, D. Harwood, and L. S. Davis, "Non-parametric model for background subtraction," in Proc. of the 6th European Conf. on Computer Vision - Part II, Lecture Notes in Computer Science, vol. 1843, pp. 751-767, Jun. 2000.
[14] G, A. Babich and O. I. Camps, "Weighted parzen windows for pattern classification," IEEE Trans. on Pattern Analysis and Machine Intelligence,, vol. 18, no. 5, pp. 567-570, May 1996.
[15] J. Gu, J. Zhou, and D. Zhang, "A combination model for orientation field in fingerprints," Pattern Recognition, vol. 37, no. 3, pp. 543-553, Mar. 2004.
[16] A. Jain, K. Nanddakumar, and A. Ross, "Score normalization in multimodal biometric systems," Pattern Recognition, vol. 38, no. 12, pp. 2270-2285, Dec. 2005.