Skip to main content
SpringerLink
Log in

Online Signature Verification Using Deep Learning Approach

  • Conference paper
  • First Online:
Soft Computing Applications (SOFA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1438))

Included in the following conference series:

  • 80 Accesses

Abstract

With the advent of advanced technology and its availability at a low price nowadays the biometric verification is getting a common method of person authentication. Online signature recognition is a class of biometric recognition in which a person is identified with the help of its signature. In this paper, a novel deep learning model using shortcut connections has been proposed for online signature recognition. The model has been designed with the modification of the original ResNet model. As the original ResNet model has been designed for the image data so after performing various experiments the modifications in the original ResNet model have been performed that are essential to process the text-based data instead of images. The proposed model has been trained and tested on a custom collected dataset of 4200 online signatures from 280 subjects to achieve an accuracy of 98.6%. It is evident from the results that the proposed model outperforms the state-of-the-art models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Saffar, M.H., Fayyaz, M., Sabokrou, M. Fathy, M.: Online signature verification using deep representation: a new descriptor. arXiv preprint arXiv:1806.09986 (2018)

  2. Guru, D.S., Manjunatha, K.S., Manjunath, S., Somashekara, M.T.: Interval valued symbolic representation of writer dependent features for online signature verification. Expert Syst. Appl. 80, 232–243 (2017)

    Article  Google Scholar 

  3. Vorugunti, C.S., Guru, D.S. Pulabaigari, V.: An efficient online signature verification based on feature fusion and interval valued representation of writer specific features. In: 2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA), pp. 1–9 (2019)

    Google Scholar 

  4. Al-Hmouz, R., Pedrycz, W., Daqrouq, K., Morfeq, A., Al-Hmouz, A.: Quantifying dynamic time warping distance using probabilistic model in verification of dynamic signatures. Soft. Comput. 23(2), 407–418 (2017). https://doi.org/10.1007/s00500-017-2782-5

    Article  Google Scholar 

  5. Malik, M.I., Liwicki, M., Dengel, A.: Evaluation of Local and Global Features for Offline Signature Verification. In: AFHA, pp. 26–30 (2011)

    Google Scholar 

  6. Ansari, A.Q., Hanmandlu, M., Kour, J., Singh, A.K.: Online signature verification using segment-level fuzzy modelling. IET Biomet. 3(3), 113–127 (2013)

    Article  Google Scholar 

  7. Okawa, M.: Template matching using time-series averaging and DTW with dependent warping for online signature verification. IEEE Access 7, 81010–81019 (2019)

    Article  Google Scholar 

  8. Jain, P., Gangrade, J.: Online signature verification using energy angle and directional gradient feature with neural network. Int. J. Innov. Res. Sci. Eng. Technol. 2(9), 4655–4660 (2013)

    Google Scholar 

  9. Fierrez, J., Ortega-Garcia, J., Ramos, D., Gonzalez-Rodriguez, J.: HMM-based on-line signature verification: feature extraction and signature modeling. Pattern Recogn. Lett. 28(16), 2325–2334 (2007)

    Article  Google Scholar 

  10. Barkoula, K., Economou, G., Fotopoulos, S.: Online signature verification based on signatures turning angle representation using longest common subsequence matching. Int. J. Doc. Anal. Recogn. 16(3), 261–272 (2013)

    Article  Google Scholar 

  11. Menotti, D., et al.: Deep representations for iris, face, and fingerprint spoofing detection. IEEE Trans. Inf. Forensics Secur. 10(4), 864–879 (2015)

    Article  Google Scholar 

  12. Liu, N., Zhang, M., Li, H., Sun, Z., Tan, T.: DeepIris: Learning pairwise filter bank for heterogeneous iris verification. Pattern Recogn. Lett. 82, 154–161 (2016)

    Article  Google Scholar 

  13. Liu, Y., Ling, J., Liu, Z., Shen, J., Gao, C.: Finger vein secure biometric template generation based on deep learning. Soft. Comput. 22(7), 2257–2265 (2018)

    Article  Google Scholar 

  14. Sam, S.M., Kamardin, K., Sjarif, N.N.A., Mohamed, N.: Offline signature verification using deep learning convolutional neural network (CNN) architectures GoogLeNet inception-v1 and inception-v3. Procedia Comput. Sci. 161, 475–483 (2019)

    Article  Google Scholar 

  15. Lai, S., Jin, L.: Recurrent adaptation networks for online signature verification. IEEE Trans. Inf. Forensics Secur. 14(6), 1624–1637 (2018)

    Article  Google Scholar 

  16. He, K. Zhang, X. Ren, S. Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778 (2016)

    Google Scholar 

  17. Liwicki, M., et al.: Signature verification competition for online and offline skilled forgeries (sigcomp2011). In International Conference on Document Analysis and Recognition (ICDAR), pp. 1480–1484 (2011)

    Google Scholar 

  18. Matsuda, K., Ohyama, W., Wakabayashi, T.: Multilingual-signature verification by verifier fusion using random forests. In: 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR), pp. 941–946 (2017)

    Google Scholar 

  19. Ferrer, M.A., et al.: Static and dynamic synthesis of Bengali and Devanagari signatures. IEEE Trans, Cybern. 48(10), 2896–2907 (2017)

    Article  Google Scholar 

  20. Kancharla, K., Kamble, V., Kapoor, M.: Handwritten signature recognition: a convolutional neural network approach. In: 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), pp. 1–5 (2018)

    Google Scholar 

  21. Tolosana, R. Vera-Rodriguez, R. Ortega-Garcia, J. Fierrez, J.: Increasing the robustness of biometric templates for dynamic signature biometric systems. In: 2015 International Carnahan Conference on Security Technology (ICCST), pp. 229–234 (2015)

    Google Scholar 

  22. Alajrami, E., et al.: Handwritten signature verification using deep learning. Int. J. Academic Multidiscip. Res. 3(12), 39–44 (2020)

    Google Scholar 

  23. Çalik, N., Kurban, O.C., Yilmaz, A.R., Yildirim, T., Ata, L.D.: Large-scale offline signature recognition via deep neural networks and feature embedding. Neurocomputing 359, 1–14 (2019). https://doi.org/10.1016/j.neucom.2019.03.027

    Article  Google Scholar 

  24. Hafemann, L.G., Sabourin, R., Oliveira, L.S.: Learning features for offline handwritten signature verification using deep convolutional neural networks. Pattern Recognit. 70, 163–176 (2017). https://doi.org/10.1016/j.patcog.2017.05.012

    Article  Google Scholar 

  25. Ponce-Hernandez, W., Blanco-Gonzalo, R., Liu-Jimenez, J., Sanchez-Reillo, R.: Fuzzy vault scheme based on fixed-length templates applied to dynamic signature verification. IEEE Access 8, 11152–11164 (2020). https://doi.org/10.1109/ACCESS.2020.2965165

    Article  Google Scholar 

  26. Dutta, S., Saini, R., Kumar P., Roy, P.P.: An efficient approach for recognition and verification of on-line signatures using PSO. In: 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR), pp. 882–887 (2017)

    Google Scholar 

  27. Antal, M., Szabó, L.Z., Tordai, T.: Online signature verification on MOBISIG finger-drawn signature corpus. Mobile Inform. Syst. 2018, 1–15 (2018)

    Article  Google Scholar 

  28. Bharadi, V.A., Sedamkar, R.R., Jangid, P.S.: Performance analysis of grid & texture based feature vector for dynamic signature recognition. In: 2015 International Conference on Pervasive Computing (ICPC), pp. 1–6 (2015)

    Google Scholar 

Download references

Acknowledgement

We are thankful to the University of Sindh, Jamshoro, Pakistan, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan for providing research environment and resources and all the volunteers who contributed in the development of the online signature dataset.

Author information

Authors and Affiliations

  1. University of Sindh, Jamshoro, Pakistan

    Mehwish Leghari, Shahzad Memon & Lachhman Das Dhomeja

  2. Quaid-e-Awam University of Engineering, Science and Technology Nawabshah, Sindh, Pakistan

    Mehwish Leghari, Akhtar Hussain Jalbani & Asghar Ali Chandio

Authors
  1. Mehwish Leghari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shahzad Memon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lachhman Das Dhomeja
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Akhtar Hussain Jalbani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Asghar Ali Chandio
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehwish Leghari .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  2. Aurel Vlaicu University of Arad, Arad, Romania

    Lakhmi C. Jain

  3. Faculty of Engineering, Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Marius Mircea Balas

  4. Cankaya University, Ankara, Türkiye

    Dumitru Baleanu

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Leghari, M., Memon, S., Das Dhomeja, L., Jalbani, A.H., Chandio, A.A. (2023). Online Signature Verification Using Deep Learning Approach. In: Balas, V.E., Jain, L.C., Balas, M.M., Baleanu, D. (eds) Soft Computing Applications. SOFA 2020. Advances in Intelligent Systems and Computing, vol 1438. Springer, Cham. https://doi.org/10.1007/978-3-031-23636-5_35

Download citation

Publish with us

Policies and ethics

Search

Navigation