Skip to main content

Offline Signature Verification Using Feature Learning and One-Class Classification

Part of the Communications in Computer and Information Science book series (CCIS,volume 1322)

Abstract

Offline signature verification remains the most commonly employed authentication modality and enjoys global acceptance. From the view point of computerized verification, concluding the authenticity of a signature offers a challenging problem for the pattern classification community. A major proportion of computerized solutions treat signature verification as a two-class classification problem where both genuine and forged signatures are employed for training purposes. For most of the real world scenarios however, only genuine signatures of individuals are available. This paper presents a signature verification technique that relies only on genuine signature samples. More precisely, we employ convolutional neural networks for learning effective feature representations and a one-class support vector machine that learns the genuine signature class for each individual. Experiments are carried out in a writer-dependent as well as writer-independent mode and low error rates are reported by only employing genuine signatures in the training sets.

Keywords

  • Feature learning
  • One-class classification
  • Signature verification
  • Forgery detection

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-71804-6_18
  • Chapter length: 13 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-71804-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Fig. 1.

Notes

  1. 1.

    Download Dataset:http://www.cedar.buffalo.edu/NIJ/data/signatures.rar.

  2. 2.

    Download Dataset: http://tc11.cvc.uab.es/datasets/SigWIcomp2015_1.

References

  1. Cedar signarure dataset download. www.cedar.buffalo.edu/NIJ/data/signatures.rar

  2. Ahlawat, S., Goel, A., Prasad, S., Singh, P.: Offline signature verification using local binary pattern and octave pattern. In: Fifth International Conference on Graphic and Image Processing (ICGIP 2013), vol. 9069, p. 906913. International Society for Optics and Photonics (2014)

    Google Scholar 

  3. Batista, L., Granger, E., Sabourin, R.: Dynamic selection of generative-discriminative ensembles for off-line signature verification. Pattern Recogn. 45(4), 1326–1340 (2012)

    CrossRef  Google Scholar 

  4. Brümmer, N., du Preez, J.: Application-independent evaluation of speaker detection. Comput. Speech Lang. 20(2), 230–275 (2006). Odyssey 2004: The Speaker and Language Recognition Workshop. https://doi.org/10.1016/j.csl.2005.08.001. http://www.sciencedirect.com/science/article/pii/S0885230805000483

  5. Christlein, V., Gropp, M., Fiel, S., Maier, A.: Unsupervised feature learning for writer identification and writer retrieval. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 991–997. IEEE (2017)

    Google Scholar 

  6. Coetzer, J.: Off-line signature verification. Ph.D. thesis, University of Stellenbosch, Stellenbosch (2005)

    Google Scholar 

  7. Dara, S., Tumma, P.: Feature extraction by using deep learning: a survey. In: 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 1795–1801. IEEE (2018)

    Google Scholar 

  8. 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)

  9. Dutta, A., Pal, U., Lladós, J.: Compact correlated features for writer independent signature verification. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 3422–3427. IEEE (2016)

    Google Scholar 

  10. El-Yacoubi, A., Justino, E., Sabourin, R., Bortolozzi, F.: Off-line signature verification using HMMs and cross-validation. In: Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No. 00TH8501), vol. 2, pp. 859–868. IEEE (2000)

    Google Scholar 

  11. Hafemann, L.G., Oliveira, L.S., Sabourin, R.: Fixed-sized representation learning from offline handwritten signatures of different sizes. Int. J. Doc. Anal. Recogn. (IJDAR) 21(3), 219–232 (2018)

    CrossRef  Google Scholar 

  12. Hafemann, L.G., Sabourin, R., Oliveira, L.S.: Analyzing features learned for offline signature verification using deep CNNs. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2989–2994. IEEE (2016)

    Google Scholar 

  13. Hafemann, L.G., Sabourin, R., Oliveira, L.S.: Writer-independent feature learning for offline signature verification using deep convolutional neural networks. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2576–2583. IEEE (2016)

    Google Scholar 

  14. 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)

    CrossRef  Google Scholar 

  15. Hafemann, L.G., Sabourin, R., Oliveira, L.S.: Offline handwritten signature verification-literature review. In: 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1–8. IEEE (2017)

    Google Scholar 

  16. Hamadene, A., Chibani, Y.: One-class writer-independent offline signature verification using feature dissimilarity thresholding. IEEE Trans. Inf. Forensics Secur. 11(6), 1226–1238 (2016)

    CrossRef  Google Scholar 

  17. Jarad, M., Al-Najdawi, N., Tedmori, S.: Offline handwritten signature verification system using a supervised neural network approach. In: 2014 6th International Conference on Computer Science and Information Technology (CSIT), pp. 189–195 (2014)

    Google Scholar 

  18. Justino, E.J., Bortolozzi, F., Sabourin, R.: A comparison of SVM and HMM classifiers in the off-line signature verification. Pattern Recogn. Lett. 26(9), 1377–1385 (2005)

    CrossRef  Google Scholar 

  19. Justino, E.J., El Yacoubi, A., Bortolozzi, F., Sabourin, R.: An off-line signature verification system using hidden Markov model and cross-validation. In: Proceedings of the 13th Brazilian Symposium on Computer Graphics and Image Processing (Cat. No. PR00878), pp. 105–112. IEEE (2000)

    Google Scholar 

  20. Kumar, A., Bhatia, K.: A survey on offline handwritten signature verification system using writer dependent and independent approaches. In: 2016 2nd International Conference on Advances in Computing, Communication, & Automation (ICACCA)(Fall), pp. 1–6. IEEE (2016)

    Google Scholar 

  21. Malik, M.I., et al.: ICDAR2015 competition on signature verification and writer identification for on- and off-line skilled forgeries (SigWIcomp2015). In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 1186–1190, August 2015

    Google Scholar 

  22. Malik, M.I., Liwicki, M.: From terminology to evaluation: performance assessment of automatic signature verification systems. In: 2012 International Conference on Frontiers in Handwriting Recognition, pp. 613–618. IEEE (2012)

    Google Scholar 

  23. Malik, M.I., Liwicki, M., Dengel, A.: Part-based automatic system in comparison to human experts for forensic signature verification. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 872–876. IEEE (2013)

    Google Scholar 

  24. Mohammed, R.A., Nabi, R.M., Sardasht, M., Mahmood, R., Nabi, R.M.: State-of-the-art in handwritten signature verification system. In: 2015 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 519–525. IEEE (2015)

    Google Scholar 

  25. Nam, S., Park, H., Seo, C., Choi, D.: Forged signature distinction using convolutional neural network for feature extraction. Appl. Sci. 8(2), 153 (2018)

    CrossRef  Google Scholar 

  26. Nemcek, W.F., Lin, W.C.: Experimental investigation of automatic signature verification. IEEE Trans. Syst. Man Cybern. 1, 121–126 (1974)

    CrossRef  Google Scholar 

  27. Niu, X.X., Suen, C.Y.: A novel hybrid CNN-SVM classifier for recognizing handwritten digits. Pattern Recogn. 45(4), 1318–1325 (2012)

    CrossRef  Google Scholar 

  28. Okawa, M.: Offline signature verification based on bag-of-visual words model using KAZE features and weighting schemes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 184–190 (2016)

    Google Scholar 

  29. Oliveira, L.S., Justino, E., Freitas, C., Sabourin, R.: The graphology applied to signature verification. In: 12th Conference of the International Graphonomics Society, pp. 286–290 (2005)

    Google Scholar 

  30. Plamondon, R., Lorette, G.: Automatic signature verification and writer identification-the state of the art. Pattern Recogn. 22(2), 107–131 (1989)

    CrossRef  Google Scholar 

  31. Pourshahabi, M.R., Sigari, M.H., Pourreza, H.R.: Offline handwritten signature identification and verification using contourlet transform. In: 2009 International Conference of Soft Computing and Pattern Recognition, pp. 670–673. IEEE (2009)

    Google Scholar 

  32. Poznanski, A., Wolf, L.: CNN-N-Gram for handwriting word recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2305–2314 (2016)

    Google Scholar 

  33. Quek, C., Zhou, R.: Antiforgery: a novel pseudo-outer product based fuzzy neural network driven signature verification system. Pattern Recogn. Lett. 23(14), 1795–1816 (2002)

    CrossRef  Google Scholar 

  34. Sabourin, R., Plamondon, R., Lorette, G.: Off-line identification with handwritten signature images: survey and perspectives. In: Baird, H.S., Bunke, H., Yamamoto, K. (eds.) Structured Document Image Analysis, pp. 219–234. Springer, Heidelberg (1992). https://doi.org/10.1007/978-3-642-77281-8_10

    CrossRef  Google Scholar 

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

  36. Sanmorino, A., Yazid, S.: A survey for handwritten signature verification. In: 2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering, pp. 54–57. IEEE (2012)

    Google Scholar 

  37. Shariatmadari, S., Emadi, S., Akbari, Y.: Patch-based offline signature verification using one-class hierarchical deep learning. Int. J. Doc. Anal. Recogn. (IJDAR) 22(4), 375–385 (2019). https://doi.org/10.1007/s10032-019-00331-2

    CrossRef  Google Scholar 

  38. 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. (2018)

    Google Scholar 

  39. Souza, V.L., Oliveira, A.L., Sabourin, R.: A writer-independent approach for offline signature verification using deep convolutional neural networks features. In: 2018 7th Brazilian Conference on Intelligent Systems (BRACIS), pp. 212–217. IEEE (2018)

    Google Scholar 

  40. Tang, Y., Wu, X.: Text-independent writer identification via CNN features and joint Bayesian. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 566–571. IEEE (2016)

    Google Scholar 

  41. Yilmaz, M.B., Yanikoglu, B., Tirkaz, C., Kholmatov, A.: Offline signature verification using classifier combination of HOG and LBP features. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–7. IEEE (2011)

    Google Scholar 

  42. Zois, E.N., Alewijnse, L., Economou, G.: Offline signature verification and quality characterization using poset-oriented grid features. Pattern Recogn. 54, 162–177 (2016)

    CrossRef  Google Scholar 

  43. Zouari, R., Mokni, R., Kherallah, M.: Identification and verification system of offline handwritten signature using fractal approach. In: International Image Processing, Applications and Systems Conference, pp. 1–4. IEEE (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Shabbir, S., Malik, M.I., Siddiqi, I. (2021). Offline Signature Verification Using Feature Learning and One-Class Classification. In: Djeddi, C., Kessentini, Y., Siddiqi, I., Jmaiel, M. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2020. Communications in Computer and Information Science, vol 1322. Springer, Cham. https://doi.org/10.1007/978-3-030-71804-6_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-71804-6_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71803-9

  • Online ISBN: 978-3-030-71804-6

  • eBook Packages: Computer ScienceComputer Science (R0)