Online Signature Verification Using Angular Transformation for e-Commerce Services

Authors: Peerapong Uthansakul, Monthippa Uthansakul

Abstract:

The rapid growth of e-Commerce services is significantly observed in the past decade. However, the method to verify the authenticated users still widely depends on numeric approaches. A new search on other verification methods suitable for online e-Commerce is an interesting issue. In this paper, a new online signature-verification method using angular transformation is presented. Delay shifts existing in online signatures are estimated by the estimation method relying on angle representation. In the proposed signature-verification algorithm, all components of input signature are extracted by considering the discontinuous break points on the stream of angular values. Then the estimated delay shift is captured by comparing with the selected reference signature and the error matching can be computed as a main feature used for verifying process. The threshold offsets are calculated by two types of error characteristics of the signature verification problem, False Rejection Rate (FRR) and False Acceptance Rate (FAR). The level of these two error rates depends on the decision threshold chosen whose value is such as to realize the Equal Error Rate (EER; FAR = FRR). The experimental results show that through the simple programming, employed on Internet for demonstrating e-Commerce services, the proposed method can provide 95.39% correct verifications and 7% better than DP matching based signature-verification method. In addition, the signature verification with extracting components provides more reliable results than using a whole decision making.

Keywords: Online signature verification, e-Commerce services, Angular transformation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056931

References:

[1] M. Fairhurst, K. Cowley, and E. Sweeney, "KAPPA Automatic Signature Verification: Signature Verification Public Trials and Public Survey on Biometrics," British Technology Group, Tech. Rep., 1994.
[2] C.-C. Hsu, L.-F. Chen, P.-C. Chang, and B.-S. Jeng, "On-line chinese signature verification based on multi-expert strategy," in Proc. 32nd Int. Carnahan Conf. Security Technology, 1998, pp. 169-173.
[3] R. Plamondon and G. Lorette, "Designing an automatic signature verifier: Problem definition and system description," in Computer Processing of Handwriting, R. Plamondon and C. G. Leedham, Eds. Singapore: World Scientific, 1990, pp. 3-20.
[4] S. H. Kim, M. S. Park, and J. Kim, "Applying personalized weights to a feature set for on-line signature verification," in Proc. 3rd Int. Conf. Document Analysis and Recognition,Montreal, QC, Canada, Aug. 1995, pp. 882-885.
[5] Y. Sato and K. Kogure, "On-line signature verification based on shape, motion, and writing pressure," in Proc. IEEE Int. Conf. Pattern Recognition, vol. 2, 1982, pp. 823-826
[6] L. L. Lee, T. Berger, and E. Aviczer, "Reliable on-line human signature verification systems," IEEE Trans. Pattern Anal. Machine Intell., vol. 18, no. 6, pp. 643-647, Jun. 1996.
[7] J. R. Yu, S. H. Kim, and J. Kim, "A class learning method for signature verification using dynamic programming," J. Korea Inst. Telemat. Electron., vol. 32-B, no. 2, pp. 154-161, 1995.
[8] B. Wirtz, "Stroke-based time warping for signature verification," in Proc. Int. Conf. Document Analysis and Recognition, vol. 1, 1995, pp. 179-182.
[9] J. G. A. Dolfing, "A comparison of ligature and contextual models for hidden Markov models based on on-line handwriting recognition," in Proc. Int. Conf. Acoustics, Speech, and Signal Processing, vol. 2, 1998, pp. 1073-1076.
[10] M. Fuentes, S. Garcia-Salicetti, and B. Dorizzi, "On Line Signature Verification: Fusion of a Hidden Markov Model and a Neural Network via a Support Vector Machine," Proc. Eighth Int-l Workshop Frontiers in Handwriting Recognition, 2002, pp. 253-258, Aug. 2002.
[11] J. G. A. Dolfing, "Handwriting recognition and verification: A hidden Markov approach," Ph.D. dissertation, Technische Universiteit Eindhoven, Eindhoven, The Netherlands, 1998.
[12] N.-J. Cheng, K. Liu, K.-C. Cheng, C.-C. Tseng, and B.-S. Jeng, "Online chinese signature verification using voting scheme," in Proc. 31st Annu IEEE Int. Carnahan Conf. Security Technology, 1997, pp. 123- 126.
[13] A. Kandel, Fuzzy Techniques in Pattern Recognition. New York: Wiley, 1982.
[14] M. Nadler and E. Smith, Pattern Recognition Engineering. New York: Wiley, 1993, pp. 299-302.