Handwritten signatures are widely used and well-accepted biometrics for personal authentication. The accuracy of signature verification systems has significantly improved in the last decade, making it possible to rely on machines in particular cases or to support human experts. Yet, based on only few genuine references, signature verification is still a challenging task. The present paper provides a comprehensive comparison of two prominent string matching algorithms that can be readily used for signature verification. Moreover, it evaluates a recent cost model for string matching which turns out to be particularly well suited for the task of signature verification. On three benchmarking data sets, we show that this model outperforms the two standard models for string matching with statistical significance.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Multimodal systems combine algorithms from different categories.
Often the DTW algorithm is endowed with a Sakoe–Chiba band  in order to exclude unusual warping paths and speed up the computation.
The Euclidean distance could be replaced by any other Minkowski metric
We perform our evaluation on a 3.5 GHz Intel Core i7 with 16 GB RAM.
Ansari, A., Hanmandlu, M., Kour, J., Singh, A.: Online signature verification using segment-level fuzzy modelling. IET Biom. 3(3), 113–127 (2014)
Bansal, M., Hanmandlu, M., Kumar, P.: Iris based authentication using local principal independent components. Optik-Int. J. Light Electron Opt. 127(11), 4808–4814 (2016)
Fierrez, J., Ortega-Garcia, J., Ramos, D., Gonzales-Rodriguez, J.: Hmm-based on-line signature verification: feature extraction and signature modeling. Pattern Recognit. Lett. 28(16), 2325–2334 (2007)
Guru, D., Prakash, H.: Online signature verification and recognition: an approach based on symbolic representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 1059—1073 (2009)
Ibrahim, M., Kyan, M., Guang, L.: On-line signature verification using global features. In: 2009 Canadian Conference on Electrical and Computer Engineering (2009)
Impedovo, D., Pirlo, G.: Automatic signature verification: the state of the art’. IEEE Trans. Syst. Man Cybern. 38(5), 609–635 (2008)
Kholmatov, A., Yanikoglu, B.: Identity authentication using improved online signature verification method. Pattern Recognit. Lett. 26(15), 1400–2408 (2005)
Kholmatov, A., Yanikoglu, B.: SUSIG: an on-line signature database, associated protocols and benchmark results. Pattern Anal. Appl. 12(3), 227–236 (2009)
Levenshtein, V.: Binary codes capable of correcting deletions, insertions and reversals. Sov. Phys. Dokl. 10(8), 707–710 (1966)
Liwicki, M., Blumenstein, M., van den Heuvel, E., Berger, C., Stoel, R.D., Found, B., Chen, X., Malik, M.I.: Sigcomp11: Signature verification competition for on- and offline skilled forgeries. In: Proc. 11th Int. Conference on Document Analysis and Recognition (2011)
Martinez-Diaz, M., Fierrez, J., Krish, R., Galbally, J.: Mobile signature verification: feature robustness and performance comparison. IET Biom. 3(4), 267–277 (2014)
Mehdi, S., Arakala, A., Davis, S., Horadam, K.: Retina verification system based on biometric graph matching. IEEE Trans. Image Process. 22(9), 3625–3635 (2013)
Mehdi Lajevardi, S., Arakala, A., Davis, S., Horadam, K.: Hand vein authentication using biometric graph matching. IET Biom. 3(4), 302–313 (2014)
Nanni, L., Lumini, A.: Ensemble of parzen window classifiers for on-line signature verification. Neurocomputing 68, 217–224 (2005)
O’Gorman, L.: Comparing passwords, tokens, and biometrics for user authentication. Proc. IEEE 91(12), 2021–2040 (2003)
Oka, M., Kato, K., Xu, Y., Liang, L., Wen, F.: Scribble-a-secret: Similarity-based password authentication using sketches. In: In Proc. of the 19th International Conference on Pattern Recognition, pp. 1–4 (2008)
Ortega-Garcia J. andFierrez-Aguilar, J., Simon, D., Gonzalez, J., Faundez, M., Espinosa, V., Satue A. andHernaez, I., Igarza, J.J., Vivaracho, C., Escudero, D., Moro, Q.I.: Mcyt baseline corpus: A bimodal biometric database. IEEE Proc. Vis. Image Signal Process. Spec. Issue Biom. Internet 150(6), 395–401 (2003)
Plamondon, R., Pirlo, G., Impedovo, D.: On-line Signature Verification, chap. 27, pp. 917–947. Springer (2014)
Plamondon, R., Srihari, S.N.: Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans. PAMI 22(1), 63–84 (2000)
Riesen, K., Hanne, T., Schmidt, R.: Sketch-based user authentication with a novel string edit distance model. IEEE Trans. Syst. Man Cybern. Syst. PP(99) (2016)
Roth, J., Liu, X., Metaxas, D.: On continuous user authentication via typing behavior. IEEE Trans. Image Process. 23(10), 4611–4624 (2014)
Sae-Bae, N., Memon, N.: Online signature verification on mobile devices. IEEE Trans. Inf. Forensics Secur. 9(6), 933—947 (2014)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. Trans. Acoust. Speech Signal Process. 26, 43–49 (1978)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26, 43–49 (1978)
Stajano, F.: No more passwords. In: In Proc. Security Protocols Workshop, pp. 49–81 (2011)
Ukkonen, E.: Algorithms for approximate string matching. Inf. Control 64(1–3), 100–118 (1985)
Vivaracho-Pascual, C., Faundez-Zanuy, M., Pascual, J.M.: An efficient low cost approach for on-line signature recognition based on length normalization and fractional distances. Pattern Recognit. 42(1), 183–193 (2009)
Wong, W., Teoh, A., Kho, Y., Wong, M.: Kernel PCA enabled bit-string representation for minutiae-based cancellable fingerprint template. Pattern Recognit. 51, 197–208 (2016)
Yanikoglu, B., Kholmatov, A.: Online signature verification using fourier descriptors. Eur. J. Adv. Signal Process. (1–14) (2009)
We would like to thank Prof. Dr. Andreas Fischer for his valuable comments and help on the DTW reference system.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work has been supported by the Commission for Technology and Innovation (CTI) project Nr. 18029.1 PFES-ES and the Bern Economic Development Agency.
About this article
Cite this article
Riesen, K., Schmidt, R. Online signature verification based on string edit distance. IJDAR 22, 41–54 (2019). https://doi.org/10.1007/s10032-019-00316-1
- User authentication
- Signature verification
- String matching
- String edit distance