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UDC 004.932.2 DOI: 10.14529/ctcr150302 Skeletonization of Binary Images and Finding of Singular Points for Fingerprint Recognition V.Yu. Gudkov, South Ural State University, Chelyabinsk, Russian Federation, diana@sonda.ru D.A. Klyuev, South Ural State University, Chelyabinsk, Russian Federation, klyuev.da@gmail.com Abstract The paper reviews a new modified Rosenfeld skeletonization algorithm of binary fingerprint images. Singular points are allocated on the basis of the skeleton. Skeletonization and allocation of singular points are the basic procedures for solving the problem of verification and fingerprint identification. The described algorithm analyzes the neighborhood of informative image points and performs actions based on analysis. A detailed analysis of neighborhoods facilitates to remove noises that are contained in the original image and can be recognized as singular points of the fingerprint. Advantages of the new method of skeletonization compared to the known described in this work are also examined. The method is implemented in C++. The procedures to visualize the final state of the skeleton are developed for monitoring the quality of its creation. Keywords skeletonization, singular points, skeletonization template, fingerprint References 1. Maltoni D., Maio D., Jain A.K., Prabhakar S. Handbook of Fingerprint Recognition. New York, Springer-Verlag, 2003. 510 p. 2. Hirzel J., Hartung D., Busch C. Fingerprint Recognition with Cellular Partitioning and Co-Sinusoidal Triplets. Biometrics and Electronic Signatures – BIOSIG, 2010, pp. 109–114. 3. Asker M. Bazen. Fingerprint Identification – Feature Extraction, Matching, and Database Search. Univ. of Twente, Enschede, The Netherlands, 2002. 187 p. 4. Wang W., Li J., Huang F., Feng H. Design and Implementation of Log-Gabor Filter in Fingerprint Image Enhancement. Pattern Recognition Letters, vol. 29, no. 3, pp. 301–308. DOI: 10.1016/j.patrec.2007.10.004 5. Klubkov I.M. (Application of the Wave Algorithm for Finding the Skeleton Bitmap). Bulletin of Don State Technical University, 2001, vol. 1, no. 1 (7), pp. 126–133 (in Russ.) 6. Gonzales R., Woods R. Tsifrovaya obrabotka izobrazheniy (Digital Processing of the Images). Мoscow, Tekhnosfera Publ., 2005. 1072 p. 7. Pfalz J.L., Rosenfeld A. Computer Representation of Planar Regions by their Skeletons. Communications of the Association for Computing Machinery, 1967, vol. 10, no. 2, pp. 119–125. DOI: 10.1145/363067.363120 Source Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control, Radio Electronics, 2015, vol. 15, no. 3, pp. 11-17. (in Russ.) (Computer Science and Engineering) |