Online Signature Verification Using Angular Transformation for e-Commerce Services
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.
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:51424", author = "Peerapong Uthansakul and Monthippa Uthansakul", title = "Online Signature Verification Using Angular Transformation for e-Commerce Services", 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.", volume = "4", number = "1", pages = "34-6", }