Siamese Network for Learning Genuine and Forged Offline Signature Verification

  • Amruta B. JagtapEmail author
  • Dattatray D. Sawat
  • Ravindra S. Hegadi
  • Rajendra S. Hegadi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)


This work aims to use a Siamese network to verify between genuine and forged signatures by making signature embeddings more robust. Currently, the Siamese network is most widely used in many applications such as Dimensionality reduction, Learning image descriptor, Face recognition, Image ranking, etc. This network is termed as twin network since it consists of two similar neural networks which take two input images and shares same weights. The critical task in signature verification is to discriminate between genuine and skilled forger since forged signature differs by some precise kind of deformation. Embedding vector is generated by Siamese network and to make embedding vector more robust we propose to add statistical measures to it, which are calculated on the embedding vector itself. The contrastive loss function is then applied on the resultant embedding vector.


Siamese network Deep neural network Signature verification Statistical measures 



Authors thank the Ministry of Electronics and Information Technology (MeitY), New Delhi for granting Visvesvaraya Ph.D. fellowship through file no. PhD-MLA\(\backslash \)4(34)\(\backslash \)2015-16 Dated: 05/11/2015.


  1. 1.
    Bhattacharya, I., Ghosh, P., Biswas, S.: Offline signature verification using pixel matching technique. Procedia Technol. 10, 970–977 (2013)CrossRefGoogle Scholar
  2. 2.
    Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R.: Signature verification using a “siamese” time delay neural network. In: Advances in Neural Information Processing Systems, pp. 737–744 (1994)Google Scholar
  3. 3.
    Chandra, S., Maheskar, S.: Offline signature verification based on geometric feature extraction using artificial neural network. In: Recent Advances in Information Technology (RAIT). IEEE, pp. 410–414 (2016)Google Scholar
  4. 4.
    Dey, S., Dutta, A., Toledo, J.I., Ghosh, S.K., Lladós, J., Pal, U.: SigNet: convolutional siamese network for writer independent offline signature verification. arXiv preprint arXiv:1707.02131 (2017)
  5. 5.
    Dutta, A., Pal, U., Lladós, J.: Compact correlated features for writer independent signature verification. In: Pattern Recognition (ICPR), pp. 3422–3427. IEEE (2016)Google Scholar
  6. 6.
    Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping, pp. 1735–1742. IEEE (2006)Google Scholar
  7. 7.
    Hafemann, L.G., Oliveira, L.S., Sabourin, R.: Fixed-sized representation learning from offline handwritten signatures of different sizes. Int. J. Doc. Anal. Recognit. (IJDAR) 21, 1–14 (2018)CrossRefGoogle Scholar
  8. 8.
    Hafemann, L.G., Sabourin, R., Oliveira, L.S.: Writer-independent feature learning for offline signature verification using deep convolutional neural networks. In: International Joint Conference on Neural Networks (IJCNN), pp. 2576–2583. IEEE (2016)Google Scholar
  9. 9.
    Jagtap, A.B., Hegadi, R.S.: Eigen value based features for offline handwritten signature verification using neural network approach. In: Santosh, K.C., Hangarge, M., Bevilacqua, V., Negi, A. (eds.) RTIP2R 2016. CCIS, vol. 709, pp. 39–48. Springer, Singapore (2017). Scholar
  10. 10.
    Jagtap, A.B., Hegadi, R.S.: Offline handwritten signature recognition based on upper and lower envelope using eigen values. In: World Congress on Computing and Communication Technologies (WCCCT). IEEE, pp. 223–226 (2017)Google Scholar
  11. 11.
    Jarad, M., Al-Najdawi, N., Tedmori, S.: Offline handwritten signature verification system using a supervised neural network approach. In: Computer Science and Information Technology (CSIT), pp. 189–195. IEEE (2014)Google Scholar
  12. 12.
    Kiani, V., Pourreza Shahri, R., Pourreza, H.R.: Offline signature verification using local radon transform and support vector machines. Int. J. Image Process. 3, 184–194 (2009)Google Scholar
  13. 13.
    Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol. 2 (2015)Google Scholar
  14. 14.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  15. 15.
    Majhi, B., Reddy, Y.S., Babu, D.P.: Novel features for off-line signature verification. Int. J. Comput. Commun. Control 1, 17–24 (2006)CrossRefGoogle Scholar
  16. 16.
    Nguyen, V., Blumenstein, M., Muthukkumarasamy, V., Leedham, G.: Off-line signature verification using enhanced modified direction features in conjunction with neural classifiers and support vector machines. In: Document Analysis and Recognition, ICDAR, vol. 2, pp. 734–738. IEEE (2007)Google Scholar
  17. 17.
    Prakash, H., Guru, D.: Offline signature verification: an approach based on score level fusion. Int. J. Comput. Appl. 1, 0975–8887 (2010)Google Scholar
  18. 18.
    Santosh, K., Nattee, C., Lamiroy, B.: Relative positioning of stroke-based clustering: a new approach to online handwritten devanagari character recognition. Int. J. Image Graph. 12(02), 1250016 (2012)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Sawat, D.D., Hegadi, R.S.: Unconstrained face detection: a deep learning and machine learning combined approach. CSI Trans. ICT 5(2), 195–199 (2017)CrossRefGoogle Scholar
  20. 20.
    Serdouk, Y., Nemmour, H., Chibani, Y.: Topological and textural features for off-line signature verification based on artificial immune algorithm. In: Soft Computing and Pattern Recognition (SoCPaR), pp. 118–122. IEEE (2014)Google Scholar
  21. 21.
    Ukil, S., Ghosh, S., Obaidullah, S.M., Santosh, K., Roy, K., Das, N.: Deep learning for word-level handwritten indic script identification. arXiv preprint arXiv:1801.01627 (2018)

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Amruta B. Jagtap
    • 1
    Email author
  • Dattatray D. Sawat
    • 1
  • Ravindra S. Hegadi
    • 1
  • Rajendra S. Hegadi
    • 2
  1. 1.Department of Computer ScienceSolapur UniversitySolapurIndia
  2. 2.Indian Institute of Information TechnologyDharwadIndia

Personalised recommendations