Abstract
Handwritten signature verification has two approaches based on online or offline data collection, both of them being supervised machine learning tasks. This work investigates the feasibility of unsupervised signature verification. It is inspired by a model-based forged signature generation approach, whose inversion could potentially provide an unsupervised solution for the signature verification task. The model inversion is attempted on a massive collection of image patches taken from samples of a large GPDSS10000 artificial signature verification dataset, pre-processed by a general-purpose deep learning network that extracts 1024 meaningful image features. An Extreme Learning Machine (ELM) solves the inversion problem at a very large scale. The paper proposes practical ways of ELM model structure selection on massive datasets and faster solvers. The results show the feasibility of an unsupervised solution for signature verification.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akusok, A., Björk, K.M., Estévez, V.: Randomized model structure selection approach for extreme learning machine applied to acid sulfate soils detection. In: To Appear in International Conference on Extreme Learning Machine. Springer (2021)
Akusok, A., Björk, K.M., Miche, Y., Lendasse, A.: High-performance extreme learning machines: a complete toolbox for big data applications. IEEE Access 3, 1011–1025 (2015)
Akusok, A., Espinosa Leal, L., Björk, K.M., Lendasse, A.: Scikit-elm: an extreme learning machine toolbox for dynamic and scalable learning. In: International Conference on Extreme Learning Machine, pp. 69–78. Springer (2019)
Akusok, A., Espinosa Leal, L., Björk, K.M., Lendasse, A., Hu, R.: Handwriting features based detection of fake signatures. In: The 14th Pervasive Technologies Related to Assistive Environments Conference, pp. 86–89 (2021)
Diaz-Cabrera, M., Morales, A., Ferrer, M.A.: Emerging issues for static handwritten signature biometric. In: Advances in Digital Handwritten Signature Processing. A Human Artefact for e-Society, pp. 111–122 (2014)
Espinosa-Leal, L., Akusok, A., Lendasse, A., Björk, K.M.: Extreme learning machines for signature verification. In: International Conference on Extreme Learning Machine, pp. 31–40. Springer (2019)
Espinosa-Leal, L., Akusok, A., Lendasse, A., Björk, K.M.: Website classification from webpage renders. In: International Conference on Extreme Learning Machine, pp. 41–50. Springer (2019)
Ferrer, M.A., Alonso, J.B., Travieso, C.M.: Offline geometric parameters for automatic signature verification using fixed-point arithmetic. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 993–997 (2005)
Ferrer, M.A., Diaz, M., Carmona-Duarte, C., Morales, A.: A behavioral handwriting model for static and dynamic signature synthesis. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1041–1053 (2017)
Ferrer, M.A., Vargas, J.F., Morales, A., Ordonez, A.: Robustness of offline signature verification based on gray level features. IEEE Trans. Inf. Forensics Secur. 7(3), 966–977 (2012)
Impedovo, D., Pirlo, G.: Automatic signature verification: the state of the art. IEEE Trans. Syst. Man Cybern. Part C (Applications and Reviews) 38(5), 609–635 (2008)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv Preprint (2015). arXiv:1502.03167
Jain, A.K., Ross, A.A., Nandakumar, K.: Introduction to Biometrics. Springer Science & Business Media (2011)
Leal, L.E., Björk, K.M., Lendasse, A., Akusok, A.: A web page classifier library based on random image content analysis using deep learning. In: Proceedings of the 11th Pervasive Technologies Related to Assistive Environments Conference, pp. 13–16 (2018)
Ortega-Garcia, J., Fierrez-Aguilar, J., Simon, D., Gonzalez, J., Faundez-Zanuy, M., Espinosa, V., Satue, A., Hernaez, I., Igarza, J.J., Vivaracho, C., et al.: MCYT baseline corpus: a bimodal biometric database. IEE Proc.-Vis. Image Signal Process. 150(6), 395–401 (2003)
Paige, C.C., Saunders, M.A.: LSQR: an algorithm for sparse linear equations and sparse least squares. ACM Trans. Math. Softw. 8(1), 43–71 (1982)
Sae-Bae, N., Memon, N.: Online signature verification on mobile devices. IEEE Trans. Inf. Forensics Secur. 9(6), 933–947 (2014)
Sharma, M., Khanna, K.: Offline signature verification using supervised and unsupervised neural networks. Int. J. Comput. Sci. Mob. Comput. 3(7), 425–436 (2014)
Acknowledgments
The authors wish to acknowledge CSC – IT Center for Science, Finland, for computational resources.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Akusok, A., Espinosa-Leal, L., Lendasse, A., Björk, KM. (2023). Unsupervised Handwritten Signature Verification with Extreme Learning Machines. In: Björk, KM. (eds) Proceedings of ELM 2021. ELM 2021. Proceedings in Adaptation, Learning and Optimization, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-031-21678-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-21678-7_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21677-0
Online ISBN: 978-3-031-21678-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)