Abstract
Online signature analysis can be widely applied in e-security and health. The latest method combines the Sigma-Lognormal model and visual feedback to extract the kinematic and spatial parameters of online signatures, but the model still does not perform well in complex handwriting signatures. Inaccurate parameters cannot reveal health information about users and cannot correctly reconstruct the online signature. This paper presents a novel Sigma-Lognormal parameter extractor for this drawback. On the one hand, this extractor estimates the parameters of pen-up and optimizes the parameters without the stroke midpoint. On the other hand, the extractor dynamically corrects the salient point position deviation by the velocity minimum point and velocity intersection point of adjacent strokes. The new extractor solves the parameter distortion caused by the ignored pen-ups and the hidden time deviation. The experiments demonstrate the accuracy and robustness of our method on multiple databases and verifiers, and the results show that the performance of the new extractor is better than the state-of-the-art method.
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References
Bhattacharya, U., Plamondon, R., Dutta Chowdhury, S., Goyal, P., Parui, S.K.: A sigma-lognormal model-based approach to generating large synthetic online handwriting sample databases. Int. J. Doc. Anal. Recognit. (IJDAR) 20(3), 155–171 (2017). https://doi.org/10.1007/s10032-017-0287-5
Carmona-Duarte, C., Ferrer, M.A., Parziale, A., Marcelli, A.: Temporal evolution in synthetic handwriting. Pattern Recogn. 68, 233–244 (2017)
Choudhury, H., Prasanna, S.M.: Representation of online handwriting using multi-component sinusoidal model. Pattern Recogn. 91, 200–215 (2019)
Dhieb, T., Ouarda, W., Boubaker, H., Halima, M.B., Alimi, A.M.: Online Arabic writer identification based on beta-elliptic model. In: 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 74–79. IEEE (2015)
Diaz, M., Fischer, A., Ferrer, M.A., Plamondon, R.: Dynamic signature verification system based on one real signature. IEEE Trans. Cybern. 48(1), 228–239 (2016)
Djeziri, S., Guerfali, W., Plamondon, R., Robert, J.: Learning handwriting with pen-based systems: computational issues. Pattern Recogn. 35(5), 1049–1057 (2002)
Duval, T., Rémi, C., Plamondon, R., Vaillant, J., O’Reilly, C.: Combining sigma-lognormal modeling and classical features for analyzing graphomotor performances in kindergarten children. Hum. Mov. Sci. 43, 183–200 (2015)
Faundez-Zanuy, M., Fierrez, J., Ferrer, M.A., Diaz, M., Tolosana, R., Plamondon, R.: Handwriting biometrics: applications and future trends in e-security and e-health. Cogn. Comput. 12(5), 940–953 (2020)
Ferrer, M.A., Diaz, M., Carmona-Duarte, C., Plamondon, R.: iDeLog: iterative dual spatial and kinematic extraction of sigma-lognormal parameters. IEEE Trans. Pattern Anal. Mach. Intell. 42(1), 114–125 (2018)
Fischer, A., Diaz, M., Plamondon, R., Ferrer, M.A.: Robust score normalization for DTW-based on-line signature verification. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 241–245. IEEE (2015)
Gomez-Barrero, M., Galbally, J., Fierrez, J., Ortega-Garcia, J., Plamondon, R.: Enhanced on-line signature verification based on skilled forgery detection using sigma-lognormal features. In: 2015 International Conference on Biometrics (ICB), pp. 501–506. IEEE (2015)
Kholmatov, A., Yanikoglu, B.: SUSIG: an on-line signature database, associated protocols and benchmark results. Pattern Anal. Appl. 12(3), 227–236 (2009)
Liwicki, M., et al.: Signature verification competition for online and offline skilled forgeries (sigcomp2011). In: 2011 International Conference on Document Analysis and Recognition, pp. 1480–1484. IEEE (2011)
Lu, X., Fang, Y., Kang, W., Wang, Z., Feng, D.D.: SCUT-MMSIG: a multimodal online signature database. In: Zhou, J., et al. (eds.) CCBR 2017. LNCS, vol. 10568, pp. 729–738. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69923-3_78
Martín-Albo, D., Plamondon, R., Vidal, E.: Improving sigma-lognormal parameter extraction. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 286–290. IEEE (2015)
O’Reilly, C., Plamondon, R.: Development of a sigma-lognormal representation for on-line signatures. Pattern Recogn. 42(12), 3324–3337 (2009)
Pertsinakis, M.: Effect of visual feedback on the static and kinematic characteristics of handwriting. J. Forensic Doc. Exam. 27, 5–21 (2017)
Plamondon, R., Alimi, A.M., Yergeau, P., Leclerc, F.: Modelling velocity profiles of rapid movements: a comparative study. Biol. Cybern. 69(2), 119–128 (1993)
Plamondon, R., O’reilly, C., Galbally, J., Almaksour, A., Anquetil, É.: Recent developments in the study of rapid human movements with the kinematic theory: applications to handwriting and signature synthesis. Pattern Recognit. Lett. 35(SI), 225–235 (2014)
Plamondon, R., O’Reilly, C., Ouellet-Plamondon, C.: Strokes against stroke–strokes for strides. Pattern Recogn. 47(3), 929–944 (2014)
Plamondon, R., Parizeau, M.: Signature verification from position, velocity and acceleration signals: a comparative study. In: 9th International Conference on Pattern Recognition, pp. 260–261. IEEE Computer Society (1988)
Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976)
Sae-Bae, N., Memon, N.: Online signature verification on mobile devices. IEEE Trans. Inf. Forensics Secur. 9(6), 933–947 (2014)
Van Gemmert, A., Plamondon, R., O’Reilly, C.: Using the sigmalognormal model to investigate handwriting of individuals with Parkinson’s disease. In: Proceedings of 16th Biennial Conference on the International Graphonomics Society, Nara Japon, pp. 119–122 (2013)
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Huang, J., Zhang, Z. (2021). A Novel Sigma-Lognormal Parameter Extractor for Online Signatures. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12823. Springer, Cham. https://doi.org/10.1007/978-3-030-86334-0_30
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