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Bagged ensemble of fuzzy classifiers and feature selection for handwritten signature verification

K.S. Sarin1, I.A. Hodashinsky1

1 Tomsk State University of Control Systems and Radioelectronics, Tomsk, Russia

 PDF, 1045 kB

DOI: 10.18287/2412-6179-2019-43-5-833-845

Pages: 833-845.

Full text of article: Russian language.

Abstract:
Handwritten signature verification is an important research area in the field of person authentication and biometric identification. There are two known methods for handwriting signature verification: if it is possible to digitize the speed of pen movement, then verification is said to be on-line or dynamic; otherwise, when only an image of handwriting is available, verification is said to be off-line or static. It is proved that when using dynamic verification, a greater accuracy is achieved than when using static verification. In the present work, the amplitudes, frequencies, and phases of the harmonics extracted from the signature signals of the X and Y coordinates of the pen movement using a discrete Fourier transform are used as characteristics of the signature. All signals are pre-processed in advance, including the elimination of gaps, the elimination of the angle of inclination, the normalization of position and scaling. A fuzzy classifier is proposed as a signature verification tool based on the features obtained. The work examines the effectiveness of this tool in the ensemble, as well as using a procedure for feature selection. To build an ensemble of classifiers, a well-known bagging method is used, and the feature selection is based on the determination of mutual information between a feature and a class of an object. Experiments on signature verification on the SVC2004 data set with the construction of a fuzzy classifier and ensembles of three, five, seven and nine fuzzy classifiers were conducted. Experiments were carried out both with the use of the feature selection procedure and without selection. The efficiency of the classifiers constructed is compared with each other and with known analogues: decision trees, support vector machines, discriminant analysis and k-nearest neighbors.

Keywords:
handwritten signature, fuzzy classifier, ensemble, bagging.

Citation:
Sarin KS, Hodashinsky IA. Bagged ensemble of fuzzy classifiers and feature selection for handwritten signature verification. Computer Optics 2019; 43(5): 833-845. DOI: 10.18287/2412-6179-2019-43-5-833-845.

Acknowledgements:
The study was financially supported under the government order of the Ministry of Education and Science of the Russian Federation in 2017-2019 No. 2.3583.2017/4.6.

References:
  1. Yang S, Yang F, Hoque S. Task sensitivity in EEG biometric recognition. Pattern Analysis and Applications 2018; 21: 105-117. DOI: 10.1007/s10044-016-0569-4.
  2. Ortega-Garcia J, Bigun J, Reynolds D, Gonzalez-Rodriguez J. Authentication gets personal with biometrics. IEEE Signal Processing Magazine 2004; 21(2): 50-62. DOI: 10.1109/MSP.2004.1276113.
  3. Ferrer MA, Diaz M, Carmona-Duarte C, Morales A. A behavioral handwriting model for static and dynamic signature synthesis. IEEE Transactions on Pattern Analysis and Machine Intelligence 2017; 39(6): 1041-1053. DOI: 10.1109/TPAMI.2016.2582167.
  4. Carmona-Duarte C, de Torres-Peralta R, Diaz M, Ferrer MA, Martin-Rincon M. Myoelectronic signal-based methodology for the analysis of handwritten. Hum Mov Sci 2017; 55: 18-30. DOI: 10.1016/j.humov.2017.07.002.
  5. Chang SH, Chen NY. Biomechanical analyses of prolonged handwriting in subjects with and without perceived discomfort. Hum Mov Sci 2015; 43: 1-8. DOI: 10.1016/j.humov.2015.06.008.
  6. TenHouten WD. Handwriting and creativity. Encyclopedia of Creativity 2011: 588-594. DOI: 10.1016/B978-0-12-375038-9.00112-6.
  7. Razzak MI, Alhaqbani B. Multilevel fusion for fast online signature recognition using multi-section VQ and time modeling. Neural Computing and Applications 2015; 26(5): 1117-1127. DOI: 10.1007/s00521-014-1779-6.
  8. Maiorana E, Campisi P, Fierrez J, Ortega-Garcia J, Neri Al. Cancelable templates for sequence-based biometrics with application to on-line signature recognition. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans 2010; 40(3): 525-538. DOI: 10.1109/TSMCA.2010.2041653.
  9. Sanchez-Reillo R, Quiros-Sandoval HC, Goicoechea-Telleria I, Ponce-Hernandez W. Improving presentation attack detection in dynamic handwritten signature biometrics. IEEE Access 2017; 5: 20463-20469. DOI: 10.1109/ACCESS.2017.2755771.
  10. Linden J, Marquis R, Taroni F. Dynamic signatures: A review of dynamic feature variation and forensic methodology. Forensic Science International 2018; 291: 216-229. DOI: 10.1016/j.forsciint.2018.08.021.
  11. Baltzakis H, Papamarkos N. A new signature verification technique based on a two-stage neural network classifier. Engineering Applications of Artificial Intelligence 2001; 14: 95-103. DOI: 10.1016/S0952-1976(00)00064-6.
  12. Ivanov AI, Lozhnikov PS, Sulavko AE. Evaluation of signature verification reliability based on artificial neural networks, Bayesian multivariate functional and quadratic forms [In Russian]. Computer Optics 2017; 41(5): 765-774. DOI: 10.18287/2412-6179-2017-41-5-765-774.
  13. Hu X, Pedrycz W, Wang X. Fuzzy classifiers with information granules in feature space and logic-based computing. Pattern Recognition 2018; 80: 156-167. DOI: 10.1016/j.patcog.2018.03.011.
  14. Hodashinsky IA, Kostyuchenko EYu, Sarin KS, Anfilofiev AE, Bardamova MB, Samsonov SS, Filimonenko IV. Dynamic-signature-based user authentication using a fuzzy classifier [In Russian]. Computer Optics 2018; 42(4): 657-666. DOI: 10.18287/2412-6179-2018-42-4-657-666.
  15. Kuncheva L. Combining pattern classifiers, Methods and algorithms. 2nd ed. New York: Wiley; 2014. ISBN: 978-1-118-31523-1.
  16. Breiman L. Bagging predictors. Machine Learning 1996; 24: 123-140. DOI: 10.1023/A:1018054314350.
  17. Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 1997; 55: 119-139. DOI: 10.1006/jcss.1997.1504.
  18. Hu J, Chen Y. Writer-independent off-line handwritten signature verification based on real adaboost. 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce 2011: 6095-6098. DOI: 10.1109/AIMSEC.2011.6010102.
  19. Bertolini D, Oliveira LS, Sabourin EJR. Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers. Pattern Recognition 2010; 43: 387-396. DOI: 10.1016/j.patcog.2009.05.009.
  20. Chandrashekar G, Sahin F. A survey on feature selection methods. Computers and Electrical Engineering 2014; 40: 16-28. DOI: 10.1016/j.compeleceng.2013.11.024.
  21. Cai J, Luo J, Wang S, Yang S. Feature selection in machine learning: A new perspective. Neurocomputing 2018; 300: 70-79. DOI: 10.1016/j.neucom.2017.11.077.
  22. Kumar R, Sharma JD, Chanda B. Writer-independent off-line signature verification using surroundedness feature. Pattern Recognition Letters 2012; 33: 302-308. DOI: 10.1016/j.patrec.2011.10.009.
  23. Cham FL, Kamins D. Signature recognition through spectral analysis. Pattern Recognition 1989; 22(1): 39-44. DOI: 10.1016/0031-3203(89)90036-8.
  24. Yanikoglu B, Kholmatov A. Online signature verification using Fourier descriptors. EURASIP Journal on Advances in Signal Processing 2009: 2009(260516). DOI: 10.1155/2009/260516.
  25. Yu L, Liu H. Feature selection for high-dimensional data: A fast correlation-based filter solution. Proceedings of the 12th International Conference on Machine Learning 2003: 856-863.
  26. Bezdek JC, Ehrlih R, Full W. FCM: the fuzzy c–means clustering algorithm. Computers & Geosciences 1984; 10(2-3): 191-203. DOI: 10.1016/0098-3004(84)90020-7.
  27. Yang X-S, Deb S. Cuckoo search via Levy flights. Proceedings of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009) 2009: 210-214.
  28. Yang X-S, Deb S. Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation 2010; 1: 330-343. DOI: 10.1504/IJMMNO.2010.035430.
  29. Yang X-S, Deb S. Cuckoo search: recent advances and applications. Neural Computing and Applications 2014; 24: 169-174. DOI: 10.1007/s00521-013-1367-1.
  30. Rokach L. Ensemble-based classifiers. Artificial Intelligence Review 2010; 33(1-2): 1-39. DOI: 10.1007/s10462-009-9124-7.
  31. Glantz SA. Primer of Biostatistics. New York: McGraw-Hill Inc; 1997.

 

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