
Technical Note: Signature Verification Algorithms: What They are, How They are Used, and How to Evaluate Them
References
- Plamondon, R. and Srihari, S.N., “Online and off-line handwriting recognition: a comprehensive survey,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, p. 63–84, Jan. 2000, doi: 10.1109/34.824821.
- Herbst, N.M., and Liu, C.N., “Automatic signature verification based on accelerometry”, IBM J. Re,. Develop., 21 (1977)245–253. 10.1147/rd.213.0245
- Guyon, I., Albrecht, P., LeCun Y., Denker, J.S. and Hubbard, W., “A Time Delay Neural Network Character Recognizer for a Touch Terminal”, Pattern Recognition, (1990).
- Lorette, G. and Plamondon, R.,
“Dynamic approaches to handwritten signature verification” , in Computer processing of handwriting, Eds. R. Plamondon and C. G. Leedham, World Scientific, 1990. 10.1142/9789814439329_0002 - Gideon, S.J., Kandulna, A., Kujur, A.A., Diana, A., Raimond, K.. Handwritten Signature Forgery Detection using Convolutional Neural Networks, Procedia Computer Science, Volume 143, 2018, Pages 978–987, ISSN 1877-0509, 10.1016/j.procs.2018.10.336.
- Malik, M.I., Liwicki, M., Dengel, A., and Found, B.. (2013). Man vs. Machine: A Comparative Analysis for Forensic Signature Verification. 9–13. 10.31974/jfde24-21-35
- Wacom. (2021, November 17). Preventing signature fraud in banking. YouTube. Retrieved July 4, 2022, from
https://www.youtube.com/watch?v=uVLruwy0rAs&t=652s - WonderNet. Electronic Signature Solutions. (n.d.). Retrieved May 12, 2022, from
https://www.wondernet.co.il/customers/ - Komarinski, P. D., Considerations for Improving Latent Print Processing (2009). NIST Latent Fingerprint Testing Workshop. NIST.
https://www.fbi.gov/services/laboratory/biometric-analysis/codis/codis-and-ndis-fact-sheet-CODIS-DNA%20Databases FBI. (2016, June 8). CODIS and NDIS Fact Sheet. FBI. Retrieved May 9, 2022, https://www.fbi.gov/services/laboratory/biometric-analysis/codis/codis-and-ndis-factsheet#CODIS-DNA%20Databases- Daniel, O., Levi, A., Pertsev, R., Issan, Y., Pasternak, Z., & Cohen, A. (2022). The Next Step–a semi-automatic coding and comparison system for forensic footwear impressions. Forensic Science International,
111378 . 10.1016/j.forsciint.2022.111378. - Daniel Ramos, D., Haraksim, R., Meuwly, D., Likelihood ratio data to report the validation of a forensic fingerprint evaluation method, Data in Brief, Volume 10, 2017, Pages 75–92, ISSN 2352-3409, 10.1016/j.dib.2016.11.008.
Henry Swofford:Toward Computational Algorithms in Forensic Fingermark Examination: Navigating a Path Forward , EAFS 2022, Stockholm, 30–05, 3-06-2022European Network of Forensic Science Institutes. Appendix 3—Overview procedure for forensic handwriting examinations and comparisons . In: European Network of Forensic Science Institutes, editor. Best practice manual for the forensic examination of handwriting. Version 03. Wiesbaden: ENFHEX; 2020. p. 26–38.- Strach, S., Novotny, M., Devlin, A., Graydon, C., and Westwood, P. (2016) Development of a Draft Methodology for Electronic Signature Examination using Wacom SignatureScope, Presented at the 74th Annual General Meeting of the American Society of Questioned Document Examiners, Pensacola Beach, Florida,
August 20-25 2016 - Kalantzis, N., Platt, A.W.G.. Digitally captured signatures: A method for the normalization of force through calibration and the use of the zeta function. J Forensic Sci. 2022; 67: 651–668. 10.1111/1556-4029.14927
- Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P., “Gradient-based learning applied to document recognition,” in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, Nov. 1998, doi: 10.1109/5.726791.
- Impedovo, D., & Pirlo, G. (2008). Automatic signature verification: The state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(5), 609–635. 10.1109/tsmcc.2008.923866
- Christophe Champod, Didier Meuwly, Forensic biometrics: Let’s face it, EAFS 2022, Stockholm, 30–05, 3-06-2022
- Kelly, J. S., & Lindblom, B. S. (2006). Scientific Examination of questioned documents (Second Edition). CRC/Taylor & Francis.
- Kam, M., Abichandani, P. and Hewett, T. (2015), Simulation Detection in Handwritten Documents by Forensic Document Examiners. J Forensic Sci, 60: 936–941. 10.1111/1556-4029.12801
- S. Lam, L. Javanbakth and S. N. Srihari, “Anatomy of a Form Reader,” Proceedings Second International Conference on Document Analysis and Recognition, Tsukuba, Japan, pp. 506–509. 10.1109/ICDAR.1993.395685
- Kalantzis N. Normalization and comparability of digitally captured signatures (DCS). In: Proceedings of the 73rd Annual Scientific Meeting of the American Academy of Forensic Sciences;
2021 Feb 15–19 ; held virtually. Colorado Springs, CO:American Academy of Forensic Sciences ; 2021. p. 717. - Bromley, J., Bentz, J., Bottou, L., Guyon, I., Lecun, Y., Moore, C., Säckinger, E., & Shah, R. (1993). Signature verification using a “Siamese” time delay neural network. International Journal of Pattern Recognition and Artificial Intelligence, 07(04), 669–688. 10.1142/s0218001493000339
DOI: https://doi.org/10.69525/jasqde.281 | Journal eISSN: 1524-7287
Language: English
Page range: 3 - 8
Published on: Jun 1, 2022
Published by: American Society of Questioned Document Examiners
In partnership with: Paradigm Publishing Services
© 2022 Kevin P. Kulbacki, Nikolaos Kalantzis, Batya Miller Fuchs, published by American Society of Questioned Document Examiners
This work is licensed under the Creative Commons Attribution 4.0 License.