Abstract
Signature verification is the major research topic in the area of biometric authentication. Signature is a behavioral attribute based on ones behavior. In this a given input is examined and is either rejected as forgery or accepted as genuine. To the best of our knowledge no work has been done on online signature verification of Indian Languages. This paper deals with the on-line signature verification of Punjabi signatures. A digitizing tablet with stylus is used for acquiring signatures online. Support vector machines were used for recognition of Signatures. The performance of the system was explored by radial basis function in which grid optimization is used. Numbers of experiments are performed by increasing the number of samples and it has been found that the accuracy of the system increases as more and more number of samples are trained. Experiments were performed by using different gamma values to obtain error rates.
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References
Alhaddad, M.J.: Multiple Classifiers to verify the Online Signature. World of Computer Science and Information Technology Journal (WCSIT) 2(2), 46–50 (2012)
Azlina, F., Mardiana, B., Zahariah, A.M., Haroon, H.: Signature Verification System using Support Vector Machine. In: International Symposium on Mechatronics and its Applications, pp. 1–4. IEEE (2009)
Mauceri, A.J.: Feasibility Studies of Personal Identification by Signature Verification. Report no. SID 65 24 RADC TR 65 33, Space and Information System Division, North American Aviation Co., Anaheim, USA (1965)
Sternberg, J.: Automatic signature verification using handwriting pressure, 1975 Wescon Tech Papers, Paper No. 31/4, Los Angeles (1975)
Herbst, N.M., Liu, C.N.: Automatic signature verification based on accelerometry. IBM J. Res. and Devel. 21, 245–253 (1977)
Lin, C.N., Herbst, N.M., Anthony, N.J.: Automatic signature verification: System description and field test results. IEEE Trans.Syst. Man, Cybern. SMC-9(1), 35–38 (1979)
Kour, J., Hanmandlu, M., Ansari, A.Q.: Online signature verification using GA-SVM. In: International Conference on Image Information Processing (ICIIP), pp. 1–4. IEEE (2011)
Ito, T., Ohyama, W., Wakabayashi, T., Kimura, F.: Combination of signature verification techniques by SVM. In: International Conference on Frontiers in Handwriting Recognition, pp. 430–433 (2012)
Parodi, M., Gómez, J.C., Liwicki, M.: Online Signature Verification Based on Legendre Series Representation. Robustness Assessment of Different Feature Combinations. In: International Conference on Frontiers in Handwriting Recognition, pp. 379–384 (2012)
Tseng, L.Y., Huang, T.H.: An Online Chinese Signature Verification Scheme Based on the ARTl Neural Network. In: International Conference on Neural Networks, vol. 3, pp. 624–630 (1992)
Fahmy, M.M.M.: Online handwritten signature verification system based on DWT features extraction and neural network classification. Ain Shams Engineering Journal 1(1), 59–70 (2010)
DSakamoto, Morita, H., Ohishi, T., Komiya, Y., Matsumot, T.: On-line Signature Verification Algorithm Incorporating Pen Position, Pen Pressure and Pen Inclination Trajectories. In: Proc. Acoustics, Speech, and Signal Processing (ICASSP), vol. 2, pp. 993–996 (2001)
Jain, A.K., Griess, F.D., Connell, S.D.: On-line signature verification. Journal of Pattern Recognition Society (12), 2963–2972 (2002)
Daramola, S.A., Ibiyemi, T.S.: Efficient on-line signature verification system. International Journal of Engineering & Technology IJET-IJENSÂ 10 (2004)
Putz-Leszczyńska, J., Kudelski, M.: Hidden Signature for DTW Signature Verification in Authorizing Payment Transactions. Journal of Telecommunications and Information Technology, 59–67 (2010)
TalalIbrahim, M., Khan, M.A., Alimgeer, K.S., Khan, M.K., Taj, I.A., Guan, L.: Velocity and pressure-based partitions of horizontal and vertical trajectories for on-line signature verification. Pattern Recognition 43, 2817–2832 (2010)
Qiao, Y., Wang, X., Xu, C.: Learning Mahalanobis Distance for DTW based Online Signature Verification. In: Proc. of the IEEE International Conference on Information and Automation, Shenzhen, China, pp. 333–338 (2011)
Muramatsu, D., Kondo, M., Sasaki, M., Tachibana, S., Matsumoto, T.: A Markov Chain Monte Carlo Algorithm for Bayesian Dynamic Signature Verification. IEEE Transactions on Information Forensics and Security 1(1), 22–34 (2006)
Fierrez, J., Ortega-Garcia, J., Ramos, D., Gonzalez-Rodriguez, J.: HMM-based on-line signature verification: Feature extraction and signature modeling. Pattern Recognition Letters 28, 2325–2334 (2007)
Zhang, D., Inagaki, S., Kanada, N., Suzuki, T.: Online Signature Verification System with Antiforgery Provision Based on Segmentation and Structure Learning of HMM. In: IEEE international conference on Systems man and cybernetics (SMC), pp. 2834–2840 (2010)
Pal, S., Alireza, A., Pal, U., Blumenstein, M.: Off-line Signature Identification Using Background and Foreground Information. In: International Conference on Digital Image Computing Techniques and Applications (DICTA), pp. 672–677 (2011)
Pal, S., Blumenstein, M., Pal, U.: Hindi Off-line Signature Verification. In: International Conference on Frontiers in Handwriting Recognition, pp. 373–378 (2012)
Pal, S., Pal, U., Blumenstein, M.: Off-line verification technique for Hindi signatures. IET Biometrics 2(4), 182–190 (2013)
Ethnologue. Indo-Aryan Classification of 221 languages that have been assigned to the Indo Aryan grouping of the Indo-Iranian branch of the Indo-European languages
Lei, H., Govinaraju, V.: A study on the consistency of features for online signature verification. In: SSPR/SPR, p. 444 (2004)
Sonawane, R.C., Patil, M.E.: An effective stroke feature selection method for online signature verification. In: Third International Conference on Computing Communication & Networking Technologies, pp. 1–6. IEEE (2012)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer (1995)
Vapnik, V.: Statistical Learning Theory. John Wiley & Sons, New York (1998)
Bergstra, J., Bengio, Y.: Random Search for Hyper-Parameter Optimization. J. Machine Learning Research 13, 281–305 (2012)
Hsu, C.-W., Chang, C.-C., Lin, C.J.: A practical guide to support vector classification.Technical Report, National Taiwan University (2010)
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Wadhawan, A., Kumar, D. (2014). Design and Analysis of Online Punjabi Signature Verification System Using Grid Optimization. In: Mauri, J.L., Thampi, S.M., Rawat, D.B., Jin, D. (eds) Security in Computing and Communications. SSCC 2014. Communications in Computer and Information Science, vol 467. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44966-0_24
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DOI: https://doi.org/10.1007/978-3-662-44966-0_24
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