Abstract
Automatic signature verification is one of the main modes to verify the identity of the individuals. Among the strategies to describe the signature in the verifiers, run-length features have attracted the attention of many researchers. This work aims to upgrade the classical run-length distribution as an additional representation for off-line signatures. Specifically, we add a fifth direction to the four classical directions of run-length features. Such fifth direction runs the signature in a spiral way providing an outside to inside view of the signature. This paper evaluates the performance of the new run-length direction combined with the classical ones. For classification purposes, we used a one-class support vector machine. Additionally, we study how to combine the new direction with the previous four original ones at both feature and score levels. Our results validate the use of this novel direction in run-length features in our own experiments and external international competition in signature verification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Diaz, M., Ferrer, M.A., Impedovo, D., Malik, M.I., Pirlo, G., Plamondon, R.: A perspective analysis of handwritten signature technology. ACM Comput. Surv. (CSUR) 51(6), 117 (2019)
Bouamra, W., Djeddi, C., Nini, B., Diaz, M., Siddiqi, I.: Towards the design of an offline signature verifier based on a small number of genuine samples for training. Expert Syst. Appl. 107, 182–195 (2018)
Galloway, M.M.: Texture analysis using gray level run lengths. Comput. Graph. Image Process. 4(2), 172–179 (1975)
Djeddi, C., Siddiqi, I., Souici-Meslati, L., Ennaji, A.: Text-independent writer recognition using multi-script handwritten texts. Pattern Recogn. Lett. 34(10), 1196–1202 (2013)
Djeddi, C., Siddiqi, I., Al-Maadeed, S., Souici-Meslati, L., Gattal, A., Ennaji, A.: Signature verification for offline skilled forgeries using textural features. In: 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 76–80. IEEE (2015)
Serdouk, Y., Nemmour, H., Chibani, Y.: Combination of OC-LBP and longest run features for off-line signature verification. In: Conference: Proceedings - 10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014 (2015). https://doi.org/10.1109/SITIS.2014.36
Ghanim, T., Nabil, A.: Offline signature verification and forgery detection approach. In: 13th International Conference on Computer Engineering and Systems (ICCES) (2018). https://doi.org/10.1109/ICCES.2018.8639420
Bouamra, W., Díaz, M., Ferrer, M.A., Nini, B.: Off-line signature verification using multidirectional run-length features. In: ICIST 2020, vol. 43, pp. 1:8–43:8 (2020)
Ferrer, M.A., Travieso, C., Alonso, J.: Off-line handwritten signature GPDS-960 corpus. In: Ninth International Conference on Document Analysis and Recognition, ICDAR 2007, vol. 2, pp. 764–768. IEEE (2007)
Kalera, M.K., Srihari, S., Xu, A.: Offline signature verification and identification using distance statistics. Int. J. Pattern Recogn. Artif. Intell. 18(07), 1339–1360 (2004). https://doi.org/10.1142/S0218001404003630
Guerbai, Y., Chibani, Y., Hadjadji, B.: The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters. Pattern Recogn. 48(1), 103–113 (2015). https://doi.org/10.1016/j.patcog.2014.07.016
Maergner, P., Howe, N., Riesen, K., Ingold, R., Fischer, A.: Offline signature verification via structural methods: graph edit distance and inkball models. In: ICFHR 2018, pp. 163–168 (2018)
Maergner, P., Riesen, K., Ingold, R., Fischer, A.: A structural approach to offline signature verification using graph edit distance. In: Proceedings of International Conference on Document Analysis and Recognition (ICDAR), pp. 1216–1222. IEEE (2017)
Maergner, P., et al.: Offline signature verification by combining graph edit distance and triplet networks. In: Bai, X., Hancock, E.R., Ho, T.K., Wilson, R.C., Biggio, B., Robles-Kelly, A. (eds.) S+SSPR 2018. LNCS, vol. 11004, pp. 470–480. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-97785-0_45
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)
Sharif, M., Khan, M.A., Faisal, M., Yasmin, M., Fernandes, S.L.: A framework for offline signature verification system. Best features selection approach. Pattern Recogn. Lett. 139, 50–59 (2018). https://doi.org/10.1016/j.patrec.2018.01.021
Hafemann, L.G., Sabourin, R., Oliveira, L.S.: Learning features for offline handwritten signature verification using deep convolutional neural networks. Pattern Recogn. 70, 163–176 (2017)
Hamadene, A., Chibani, Y.: One-class writer-independent off-line signature verification using feature dissimilarity thresholding. IEEE Trans. Inf. Forensics Secur. 11(6), 1 (2016). https://doi.org/10.1109/TIFS.2016.2521611
Das, A., Suwanwiwat, H., Pal, U., Blumenstein, M.: ICFHR 2020 Competition on Short answer ASsessment and Thai Student SIGnature and Name COMponents Recognition and Verification (SASIGCOM 2020), ICFHR 2020, pp. 222–227 (2020)
Zhu, Y., Lai, S., Li, Z., Jin, L.: Point-to-set similarity based deep metric learning for offline signature verification. In: ICFHR 2020 (2020)
Hadjadj, I., Gattal, A., Djeddi, C., Ayad, M., Siddiqi, I., Abass, F.: Offline signature verification using textural descriptors. In: Morales, A., Fierrez, J., Sánchez, J.S., Ribeiro, B. (eds.) IbPRIA 2019. LNCS, vol. 11868, pp. 177–188. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-31321-0_16
Das, A.: Face recognition in reduced Eigen-plane. In: Proceedings of the 2012 International Conference on Communications, Devices and Intelligent Systems, CODIS 2012, pp. 620–623. IEEE (2012)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(10), 62–66 (1979). https://doi.org/10.1109/TSMC.1979.4310076
Impedovo, D., Pirlo, G.: Automatic signature verification: the state of the art. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 38(5), 609–635 (2008)
Wirtz, B.: Technical evaluation of biometric systems. In: Chin, R., Pong, T.-C. (eds.) ACCV 1998. LNCS, vol. 1351, pp. 499–506. Springer, Heidelberg (1997). https://doi.org/10.1007/3-540-63930-6_160
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Bouamra, W., Diaz, M., Ferrer, M.A., Nini, B. (2022). Spiral Based Run-Length Features for Offline Signature Verification. In: Carmona-Duarte, C., Diaz, M., Ferrer, M.A., Morales, A. (eds) Intertwining Graphonomics with Human Movements. IGS 2022. Lecture Notes in Computer Science, vol 13424. Springer, Cham. https://doi.org/10.1007/978-3-031-19745-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-19745-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19744-4
Online ISBN: 978-3-031-19745-1
eBook Packages: Computer ScienceComputer Science (R0)