Search Space Reduction in Fingerprint Recognition Based on Block Orientation Field
Subject Areas : electrical and computer engineeringS. Helfroush 1 , H. Ghassemian 2
1 -
2 - Tarbiat Modares University
Keywords: Fingerprintcontinuous classificationrecognitionfingercode,
Abstract :
Classification is the first essential step in every automatic fingerprint recognition system. Regarding to the time and expense of recognition process, it has the benefit of search space reduction. Conventional classification methods are based on visible fingerprint classes. However, due to small number of these classes and nonuniform distribution of fingerprints among them, continuous classification scheme has been addressed. In this method, a similarity criterion is defined and a degree of likeness is assigned to the similarity of input fingerprint and each fingerprint in database. According to similarity criterion, matching of input fingerprint is begun first with the image in database that is more similar to input fingerprint. In this paper, a new similarity measuring method is proposed and used for continuous classification of fingerprints. The method is based on block orientation field. It is translation and rotation invariant and does not need core point existence and detection. Experimental results on FVC2000 database demonstrate the effectiveness of the proposed algorithm in search space reduction compared with the other methods.
[1] D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Hand Book of Fingerprint Recognition, Springer, New York, pp. 173-202, 2003.
[2] H. Ghassemian, "An automatic fingerprint classification algorithm," Esteghlal J. of Engineering, vol. 18, no. 1, pp. 1-11, Sep. 1999.
[3] J. Qi, S. Young, and Y. Wang, "Fingerprint matching combining the global orientation field with minutia," Pattern Recognition Letters, vol. 26, no. 15, pp. 2424-2430, 2005.
[4] N. Yager and A. Amin, "Evaluation of fingerprint orientation field registration algorithms," in Proc. 17th Int. Conf. on Pattern Recognition ICPR’04, pp. 721-724, 2004.
[5] H. Ghassemian, "A robust structural fingerprint restoration," International J. of Engineering, vol. 10, no. 14, pp. 182-190, Nov. 1997.
[6] T. Kamei and M. Mizoguchi, "Fingerprint preselection using eigen features," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 918-923, 1998.
[7] J. D. Boer, A. M. Bazen, and S. H. Gerez, "Indexing fingerprint databases based on multiple features," in Proc. Workshop on Circuits, Systems and Signal Processing ProRISC 2001, pp. 300-306, 2001.
[8] R. Cappeli, D. Maio, and D. Maltoni, "Combining fingerprint classifiers," in Proc. 1st Int. Workshop on Multiple Classifier Systems, pp. 351-361, 2000.
[9] B. Bhanu and X. Tan, "Fingerprint indexing based on novel features of minutiae triplets," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 402-421, May 2003.
[10] A. K. Jain, S. Probhakar, L. Hong, and S. Pankanti, "Filter bank-based fingerprint matching," IEEE Trans. on Image Processing, vol. 9, no. 5, pp. 846-859, 2000.
[11] ص. هلفروش و ح. قاسميان، "تشخيص غير حساس به چرخش اثر انگشت به کمک تصوير بلوک جهتي،" مجموعه مقالات سيزدهمين کنفرانس مهندسي برق ايران، 2005ICEE، جلد اول، صفحه 198-202، ارديبهشت 1384.
[12] A. M. Bazen and S. H. Gerez, "Systematic methods for the computation of the directional fields and singular points of fingerprints," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 905-919, Jul. 2002.
[13] Y. He, J. Tian, X. Luo, and T. Zhang, "Image enhancement and minutiae matching in fingerprint verification," Pattern Recognition Letters, vol. 24, no. 9, pp. 1349-1360, Jun. 2003.
[14] H. Ghassemian, "A robust on-line restoration Algorithm for fingerprint segmentation," in Proc. IEEE Int. Conf. on Image Processing, vol. 2, pp. 181-184, Sep. 1996.
[15] D. Maio, A. K. Jain, "FVC2000: fingerprint verification competition," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 3, pp. 402-412, Mar. 2002.
