Accelerated Fingerprint Identification Method

Vladimir J. Gudkov

Abstract


The article outlines a method for accelerated identification of fingerprint images based on templates as image models. They are formed as a result of automatic processing of images. The method is based on the properties of the nearest neighborhoods of minutiae in the form of endings and bifurcations and consists of two stages. At the first stage, each minutia of the query template is compared with each minutia of the reference template from the database and the similarity of such pairs of minutiae are estimated. To speed up computational operations, classes are introduced that allow you quickly accumulate the similarity of minutiae from these two templates in a histogram. Histograms are built for all reference templates from the database and one query template. At the second stage, based on histogram estimates, the most similar templates are selected, the number of which is much less than the size of the database. These templates are compared additionally taking into account the consolidation of minutiae and the compactness of the location of the corresponding pairs of minutiae. Significant acceleration of the identification algorithm is achieved by discarding dissimilar pairs of minutiae at the first stage and pairs of patterns with poor histogram estimates at the second stage. The results of experiments are presented, which are published on the Internet.


Keywords


fingerprint; identification; minutia; histogram

References


Bolle R.M., Connel J.Y., Pankanti S., et al. Guide to Biometrics. New York, Springer-Verlag, 2004. 364 p. DOI: 10.1007/978-1-4757-4036-3.

Maltoni D., Maio D., Jain A.K., et al. Handbook of Fingerprint Recognition. London, Springer-Verlag, 2009. 494 p. DOI: 10.1007/978-1-84882-254-2.

ISO/IEC 19794-2:2011. Information technology – Biometric data interchange formats – Part 2: Finger minutiae data (accessed: 23.07.2020).

Bae G., Lee H., Hwang S.D., et al. Secure and Robust User Authentication Using Partial Fingerprint Matching. Proceedings of 2018 IEEE International Conference on Consumer Electronics, ICCE. 2018. P. 1–6. DOI: 10.1109/icce.2018.8326078.

Hidayat R., Souvanlit K., Bejo A. An Improvement of Minutiae-based Fingerprint Matching: Two Level of Scoring System. Proceedings of 2016 International Symposium on Electronics and Smart Devices, ISESD. 2016. P. 264–267. DOI: 10.1109/ISESD.2016.7886730.

Singh P., Kaur L. Fingerprint Feature Extraction Using Morphological Operations. Proceedings of 2015 International Conference on Advances in Computer Engineering and Applications. 2015. P. 764–767. DOI: 10.1109/ICACEA.2015.7164805.

Gudkov V.J. Methods for Mathematical Description and Identification of Fingerprints. Editor Arlazarov V.L., Emeljanov N.E. Image Processing and Data Analysis: Proceedings of ISA RSA. LIBROKOM. 2008. Vol. 38. P. 336–356. (in Russian)

Liao C.C., Chiu C.T. Fingerprint Recognition with Ridge Features and Minutiae on Distortion. Proceedings of 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP. 2016. P. 2109–2113. DOI: 10.1109/ICASSP.2016.7472049.

Barman S., Chattopadhyay S., Samanta D., et al. An Efficient Fingerprint Matching Approach Based on Minutiae to Minutiae Distance Using Indexing With Effectively Lower Time Complexity. Proceedings of 2014 International Conference on Information Technology. 2014. P. 179–183. DOI: 10.1109/ICIT.2014.46.

Tran M.H., Duong T.N., Nguyen D.M., et al. A Local Feature Vector for an Adaptive Hybrid Fingerprint Matcher. 2017 International Conference on Information and Communications, ICIC. 2017. P. 249–253. DOI: 10.1109/INFOC.2017.8001668.

Gudkov V.J. Ridge Counting Model Based on the Topology of a Fingerprint Image. Bulletin of Chelyabinsk State University. 2011. Vol. 13. P. 99–108. (in Russian)

Jiang X., Yau W.Y. Fingerprint Minutiae Matching Based on the Local and Global Structures. Proceedings of the 15th International Conference on Pattern Recognition, ICPR-2000. 2000. Vol. 2. P. 1038–1041. DOI: 10.1109/ICPR.2000.906252.

Feng Y., Feng J., Chen X., et al. A Novel Fingerprint Matching Scheme Based on Local Structure Compatibility. Proceedings of the 18th International Conference on Pattern Recognition, ICPR'06. 2006. P. 374–377. DOI: 10.1109/ICPR.2006.137.

Cao J., Feng J. A Robust Fingerprint Matching Algorithm Based on Compatibility of Star Structures. Proceedings of the Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, MIPPR 2009. 2009. Vol. 7498. Remote Sensing and GIS Data Processing and Other Applications, 74983X. DOI: 10.1117/12.832357.

Ratha N.K., Pandit V.D., Bolle R.M., et al. Robust Fingerprints Authentication Using Local Structure Similarity. Workshop on Applications of Computer Vision. 2000. P. 29–34. DOI: 10.1109/WACV.2000.895399.

Chikkerur S., Cartwright A., Govindaraju V. K-plet and CBFS: A graph Based Fingerprint Representation. Proceedings of the International Conference on Biometrics, ICB 2006: Advances in Biometrics. 2006. P. 309–315. DOI: 10.1007/11608288_42.

Chen X., Wang L., Li M. An Efficient Graph-Based Algorithm for Fingerprint Representation and Matching. Proceedings of the 3-rd International Conference on Multimedia Technology, ICMT 2013. 2013. P. 1019–1029. DOI: 10.2991/icmt13.2013.125.

Leslie S., Sumathi C.P. A Robust Hierarchical Approach to Fingerprint Matching Based on Global and Local Structures. International Journal of Applied Engineering Research. 2018. Vol. 13, no. 7. P. 4730–4739.

Capelli R., Ferrara M., Maltoni D. Fingerprint Indexing Based on Minutia Cylinder-Code. IEEE transactions on pattern analysis and machine intelligence. 2011. Vol. 33, no. 5. P. 1051–1057. DOI: 10.1109/TPAMI.2010.228.

Zheng F., Yang C. Latent Fingerprint Match using Minutia Spherical Coordinate Code, International Conference on Biometrics, ICB 2015 (Phuket, Thailand, May, 19-22, 2015). 2015. P. 357–362. DOI: 10.1109/ICB.2015.7139061.

Tabassi E., Wilson C., Watson C. Fingerprint Image Quality. NIST Internal Report 7151, National Institute for Standards and Technology, 2004. URL: https://www.nist.gov/publications/fingerprint-image-qualitiy (accessed: 23.07.2020).

Gudkov V.J., Arkabaev D.I. Method for Comparing Fingerprints of Papillary Patterns. Patent RF, no. 2331108, G 06 K 9/62, 2008. Vol. 22. (in Russian)

Novikov F.A. Discrete Mathematics for Programmers: Textbook for Higher Schools. St. Petersburg, Piter, 2009. 384 p. (in Russian)

Warren H.S. Hacker’s Delight, 2nd ed., Addison-Wesley Professional, 2018. 512 p.

Dorizzi B., Cappelli R., Ferrara M., et al. Fingerprint and On-Line Signature Verification Competitions at ICB 2009. Proceedings of the International Conference on Biometrics, ICB 2009. 2009. P. 725–732. DOI: 10.1007/978-3-642-01793-3_74.

Gudkov V.J. Methods of the First and Second Processing of Fingerprint Images. Miass, Publishing of the Geotour, 2009. 237 p. (in Russian)

FVC – ongoing: on-line evaluation of fingerprint recognition algorithms. URL: https://biolab.csr.unibo.it/FVCOnGoing/UI/Form/Home.aspx (accessed: 23.07.2020).




DOI: http://dx.doi.org/10.14529/cmse210103