Rough set approach to online signature identification

Digital Signal Processing: A Review Journal

Volumn 21, Issue 3, 2011, Pages 477-485

  Al Mayyan, Waheeda ^a   Own, Hala S ^b   Zedan, Hussein ^a  

^a DE MONTFORT UNIVERSITY   (United Kingdom)

^b National Research Institute of Astronomy and Geophysics   (Egypt)

Author keywords

Biometric recognition; Classification; Feature reduction; Na ve Bayes; Online signature identification; Rough set

Indexed keywords

CLASSIFICATION (OF INFORMATION);

BIOMETRIC RECOGNITION; CLASSIFICATION RATES; DYNAMIC PROPERTY; ESSENTIAL CHARACTERISTIC; FEATURE REDUCTION; ONLINE SIGNATURE; ROUGH SET CLASSIFIERS; SIGNATURE IDENTIFICATION;

ROUGH SET THEORY;

EID: 79954414529     PISSN: 10512004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dsp.2011.01.007     Document Type: Article

References (36)

1. D. Dessimoz, J. Richiardi, C. Champod, A. Drygajlo, Multimodal biometrics for identity documents (MBioID): State-of-the-art (version 2.0), Research Report PFS 341-08.05, 2006.
2. A. Ross, D. Nandakumar, and A.K. Jain Handbook of Multibiometrics 2006 Springer Heidelberg
3. R. Plamondon, and G. Lorette Automatic signature verification and writer identification-the state of the art Pattern Recognit. 22 2 1989 107 131
4. A. Kholmatov, and B. Yanikoglu Identity authentication using improved online signature verification method Pattern Recognit. Lett. 26 15 November 2005 2400 2408 (Pubitemid 41394296)
5. L. Yang, B. Widjaja, and R. Prasad Application of hidden Markov models for signature verification Pattern Recognit. 28 2 1995 161 170
6. J. Fierrez-Aguilar, J. Ortega-Garcia, and J. Gonzalez-Rodriguez Target dependent score normalization techniques and their application to signature verification Proceedings Biometric Authentication: First International Conference (ICBA 2004), Hong Kong, China Lecture Notes in Comput. Sci. vol. 3072 July 2004 498 504
7. D.Y. Yeung, H. Chang, Y. Xiong, S. George, R. Kashi, T. Matsumoto, and G. Rigoll SVC2004: First international signature verification competition Proc. of Intl. Conf. on Biometric Authentication, ICBA Lecture Notes in Comput. Sci. vol. 3072 2004 Springer 16 22
8. M.M. Shafiei, H.R. Rabiee, A new online signature verification algorithm using variable length segmentation and hidden Markov models, in: Proceedings of the Seventh International Conference on Document Analysis and Recognition, vol. 1, 2003, pp. 443-446.
9. L.L. Lee, Neural approaches for human signature verification, in: Proceedings of the Third International Conference on Signal Processing, 1996, pp. 1346-1349.
10. D.Z. Lejtman, S.E. George, On-line handwritten signature verification using wavelets and back-propagation neural networks, in: The 6th International Conference on Document Analysis and Recognition, Seattle, USA, September 2001.
11. M.F. Zanuy On-line signature recognition based on VQ-DTW Pattern Recognit. 40 3 2007 981 992
12. L. Nanni, and A. Lumini A novel local on-line signature verification system Pattern Recognit. Lett. 29 5 April 2008 559 568
13. J. Ortega, D. Simon, M. Faundez, V. Espinosa, A. Satue, I. Hernaez, J.-J. Igarza, C. Vivaracho, Q.-I. Moro, MCYT: A multimodal biometric database, in: Proc. COST-275 Biometric Recognition Workshop, Rome, Italy, November 2002, pp. 123-126.
14. L. Nanni, E. Maiorana, A. Lumini, and P. Campisi Combining local, regional and global matchers for a template protected on-line signature verification system Expert Syst. Appl. 37 5 May 2010 3676 3684
15. H. Raafat, A.S. Tolba, W. Almayaan, A glove-based system for automated signature identification using neural networks, in: The Fifth International Conference on Systems Analysis, and Synthesis, ISAS 99, Orlando, Florida, July 31-4 August, 1999.
16. H. Lei, V. Govindaraju, A study on the consistency of features for on-line signature verification, in: SSPR/SPR, 2004, p. 444.
17. J. Fierrez-Aguilar, L. Nanni, J. Lopez-Penalba, J. Ortega-Garcia, D. Maltoni, An on-line signature verification system based on fusion of local and global information, in: Audio- and Video-Based Biometric Person Authentication 2005, New York, USA, July 20-22, 2005, pp. 523-532. (Pubitemid 41423069)
18. C.E. Pippin, Dynamic signature verification using local and global features, Technical report, Georgia Inst. Inform. Technol., Atlanta, GA, 2004.
19. G.V. Kiran, R.S.R. Kunte, S. Samuel, On line signature verification system using probabilistic feature modelling, in: International Symposium on Signal Processing and Its Application (ISSPA), Kuala Lumpur, Malaysia, August 2001, pp. 355-358.
20. S. Krawczyk, User authentication using on-line signature and speech, Masters thesis, Michigan State University, Dep. of Computer Science and Engineering, 2005.
21. P. Domingos, and M. Pazzani On the optimality of the simple Bayesian classifier under zero-one loss Mach. Learn. 29 1997 103 130 (Pubitemid 127510035)
22. N. Friedman, D. Geiger, and M. Goldszmidt Bayesian network classifiers Mach. Learn. 29 1997 131 163 (Pubitemid 127510036)
23. Z. Pawlak Rough sets Int. J. Comput. Inform. Sci. 11 1982 341 356
24. K. Cios, W. Pedrycz, and R. Swiniarski Data Mining Methods for Knowledge Discovery 1998 Kluwer Academic Publishers
25. R.E. Kent Rough concept analysis, rough sets, fuzzy sets knowledge discovery W.P. Ziarko, Proceedings of the International Workshop on Rough, Sets, Knowledge Discovery Banff, Alta, Canada 1994 Springer 261 273
26. J. Komorowski, Lech Polkowski, and Andrzej Skowron Rough sets: A tutorial http://folli.loria.fr/cds/1999/library/pdf/skowron.pdf
27. J. Liu, Q. Hu, and D. Yu A comparative study on rough set based class imbalance learning Int. J. Knowl. Based Syst. 21 2008 753 763
28. Z. Pawlak, and A. Skowron Rudiments of rough sets Inform. Sci. 177 1 2007 3 27 (Pubitemid 44556698)
29. J. Bazan, H.S. Nguyen, S.H. Nguyen, P. Synak, and J. Wróblewski Rough set algorithms in classification problem L. Polkowski, S. Tsumoto, T.Y. Lin, Rough Set Methods and Applications 2000 Physical Verlag 49 88
30. A.E. Hassanien, H. Own, Rough sets for prostate patient analysis, in: Proceedings of International Conference on Modeling and Simulation (MS2006), Malaysia, 2006.
31. D. Slezak Various approaches to reasoning with frequency-based decision reducts: a survey L. Polkowski, S. Tsumoto, T.Y. Lin, Rough Sets in Soft Computing and Knowledge Discovery: New Developments 2000 Physical Verlag
32. J.W. Grzymala-Busse MLEM2 rule induction algorithms: With and without merging intervals Stud. Comput. Intell. (SCI) vol. 118 2008 Springer 153 164
33. J. Li, and N. Cercone A rough set based model to rank the importance of association rules Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing (RSFDGrC 99) Lecture Notes in Comput. Sci. 2005 Springer Berlin, Germany 109 118
34. N. Zhong, and A. Skowron Rough sets based knowledge discovery process Int. J. Appl. Math. Comput. Sci. 11 3 2001 603 619
35. Z. Pawlak, J. Grzymala-Busse, R. Slowinski, and W. Ziarko Rough sets Comm. ACM 38 11 1995 89 95
36. R. Gottumukkal, and V.K. Asari An improved face recognition technique based on modular PCA approach Pattern Recognit. Lett. 25 4 2004