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Signature System

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Automated Biometrics

Abstract

Handwritten signature is one of the most popular ways to verify one’s identity. In Section 10.1, we introduce some basic principles and methods of signature verification systems. Two kinds of signature systems, off-line and on-line, are discussed in Section 10.2 and 10.3, respectively. An on-line signature verification system based on Dynamic Time Warping (DTW) is described in Section 10.4. Section 10.5 will present an on-line signature verification application in the Internet/Intranet.

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References

  1. R. Plamondon and G. Lorette, “Automatic Signature Verification and Writer Identification — The State of the Art,” Pattern Recognition, vol. 1, no. 2, pp. 107–131, 1989.

    Article  Google Scholar 

  2. J. Duvernoy, “Handwritting Synthesis and Classification by Means of Space-Variant Transform and Karhunen-Loeve Analysis,” J. Opt. Soc. Am. 65, pp. 1331–1336, 1975.

    Google Scholar 

  3. N. Papamarkos and H. Baltzakis, “Off-line Signature Verification Using Multiple Neural Network Classification Structures”, Proceedings of 13th International Conference on Digital Signal Processing, 1997.

    Google Scholar 

  4. K. Han and I.K. Sethi, “Handwritten Signature Retrieval and Identification,” Pattern Recognition Letters, vol. 17, pp. 83–90, 0167-8655/96, 1996.

    Article  Google Scholar 

  5. M. Dehghan, K. Faez and M. Fathi, “Signature Verification Using Shape Descriptors and Multiple Neural Networks,” TENCON ′97, IEEE Region 10 Annual Conference, Speech and Image Technologies for Computing and Telecommunications, Proceedings of IEEE, vol. 1, pp.415-418, 1997.

    Google Scholar 

  6. J. Lin and J.G. Li, “Off-line Chinese Signature Verification”, Proceedings of International Conference on Image Processing, 1996.

    Google Scholar 

  7. G. Dimauro, S. Impedovo, G. Pirlo and A. Salzo, “A Multi-expert Signature Verification System for Bankcheck Processing,” Automatic Bankcheck Processing, pp.364-381, Wold Scientific Publishing Co. Pte. Ltd., Singapore, 1997.

    Google Scholar 

  8. R. Sabourin and G. Genest, “An Extended-Shadow-Code Based Approach for Off-Line Signature Verification: Part-II-Evaluation of Several Multi-Classifier Combination Strategies,” Proc. ICDAR, Ulm, IEEE, 1995.

    Google Scholar 

  9. K. Huang and H. Yan, “On-line Signature Verification Based on Dynamic Segmentation and Global and Local Matching,” Optical Engineering, vol. 34, no. 12, pp. 3480–3487, December 1995.

    Article  Google Scholar 

  10. J.G.A. Dolfing, E.H.L. Aarts, V. Oosterhout and J.J. G.M., “On-line Signature Verification with Hidden Markov Models”, Proceedings of 14th International Conference on Pattern Recognition, 1998.

    Google Scholar 

  11. X.H. Yang, T. Furuhashi, K. Obata and Y Uchikawa, “Constructing a High Performance Signature Verification System Using a GA Method”, Proceedings of the Second New Zealand Two-Stream Int’l Conference on Artificial Neural Networks and Expert Systems (ANNES ′95), 0-8186-7174-2/95 IEEE, 1995.

    Google Scholar 

  12. F. Bauer and B. Wirtz, “Parameter Reduction and Personalized Parameter Selection for Automatic Signature Verification,” Proc. ICDAR, Ulm, IEEE, 1995.

    Google Scholar 

  13. L.L. Lee, T. Berger, and E. Aviczer, “Reliable On-line Human Signature Verification Systems,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 6, 0162-8828/96, IEEE 1996, June 1996.

    Google Scholar 

  14. G.S.K. Fung, J.N.K. Liu, and R.W.H. Lau, “Feature Selection in Automatic Signature Verification Based on Genetic Algorithms,” Proceedings of the International Conference on Neural Information Processing, Progress in Neural Information Processing, Amari, et al (Eds), pp. 811–815, Springer-Verlag, 1996.

    Google Scholar 

  15. R. Martens and L. Claesen, “On-line Signature Verification: Discrimination Emphasised,” Proc. ICDAR, Ulm, IEEE, 1997.

    Google Scholar 

  16. B. Herbst and D. Richards, “On an Automated Signature Verification System,” Proceedings of IEEE International Symposium on Industrial Electronics, 1998.

    Google Scholar 

  17. L.L. Lee, “Neural Approaches for Human Signature Verification,” Proc. ICDAR, Ulm, IEEE 1995. 3rd International Conference on Signal Processing, 1996.

    Google Scholar 

  18. G.B. Hesketh, “COUNTERMATCH: A Nueral Network Approach to Automatic Signature Verification,” IEE Colloquium on Neural Networks for Industrial Applications (Digest No. 1997/014).

    Google Scholar 

  19. N. Mohankrishnan, W.S. Lee and M.J. Paulik, “Multi-Layer Neural Network Classification of Online Signatures,” IEEE 39th Midwest symposium on Circuits and Systems, 1996.

    Google Scholar 

  20. R. Martens and L. Claesen, “On-line Signature Verification by Dynamic Time Warping,” Proceeding of 13th International Conference on Pattern Recognition, 1015-4651/96, 1996.

    Google Scholar 

  21. R. Martens and L. Claesen, “Dynamic Programming Optimization for On-line Signature Verification,” Proceeding of 4th ICDAR ′97. 0-8186-7898-4/97, 1997.

    Google Scholar 

  22. B. Wirtz, “Stroke-Based Time Warping for Signature Verification,” Proc. ICDAR, Ulm, IEEE, 1995.

    Google Scholar 

  23. W.S. Lee, N. Mohankrishnan and M.J. Paulik, “Improved Segmentation Through Dynamic Time Warping for Signature Verification Using Neural Network Classifier,” Proceedings of 1998 International Conference on Image Processing, 1998.

    Google Scholar 

  24. B. Wirtz, “Average Prototypes for Stroke-Based Signature Verification,” Proceedings of 4th ICDAR ′97. 0-8186-7898-4/97,1997.

    Google Scholar 

  25. C.C. Hsu, L.F. Chen, P.C. Chang and B.S. Jeng, “On-line Chinese Signature Verification Based on Multi-expert Strategy,” Proceedings of 32nd Annual 1998 International Carnahan Conference on Security Technology, 1998.

    Google Scholar 

  26. R. Plamomdon, “A Model-Based Dynamic Signature Verification System,” Fundamentals in Handwriting Recognition, Proceedings of the NATO Advanced Study Institute on Fundamentals in Handwriting Recognition, France: Springer-Verlag, pp. 417–434, 1993.

    Google Scholar 

  27. M.J. Paulik, N. Mohankrishnan and M. Mikiforuk, “A Time Varying Vector Autoregressive Model for Signature Verification,” The Proceeding of The IEEE, 0-7803-2428-5/95, 1995.

    Google Scholar 

  28. F. Leclerc and R. Plamondon, “Automatic Signature Verification: The State Of The Art — 1989–1993,” Progress in Automatic Signature Verification, Singapore: World Scientific, pp. 3–20, 1994.

    Google Scholar 

  29. G. Pirlo, “Algorithms for Signature Verification, Fundamentals in Handwriting Recognition,” Proceedings of the NATO Advanced Study Institute on Fundamentals in Handwriting Recognition, France: Springer-Verlag, pp. 435–455, 1993.

    Google Scholar 

  30. B. Daniel, Advanced Techniques for Java Developers, Wiley, New York: [c1998], c1997.

    Google Scholar 

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Zhang, D.D. (2000). Signature System. In: Automated Biometrics. The International Series on Asian Studies in Computer and Information Science, vol 7. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4519-4_10

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  • DOI: https://doi.org/10.1007/978-1-4615-4519-4_10

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7038-3

  • Online ISBN: 978-1-4615-4519-4

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