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Patch-based offline signature verification using one-class hierarchical deep learning

  • Sima Shariatmadari
  • Sima EmadiEmail author
  • Younes Akbari
Original Paper
  • 112 Downloads

Abstract

Automatic processing of offline signature verification (in general) can be considered as a low-cost solution to problems in biometrics in comparison with other solutions (e. g. fingerprint, face verification, etc.). This study aims to present a novel writer-dependent approach to verifying an individual’s signature through offline image patches of their handwriting. The proposed approach is based on hierarchical one-class convolutional neural network for learning only genuine signatures with different feature levels. Since forgeries are not available for each user enrolled in a real application scenario, this study considers signature verification as a one-class problem. In addition, to achieve a clear structure in image, designing hierarchical network architecture based on the coarse-to-fine principle can lead to more precise results. With lower-level features, the network presents a higher visual quality at the boundary area revealing similarities between genuine signatures, while higher-level features can discriminate the quality of the pen strokes to predict forgeries from genuine signatures. The presented system was tested on two Persian databases (PHBC and UTSig) as well as two Latin databases (MCYT-75 and CEDAR). The results of the analyses produced by this method were generally better and more exact in terms of the four signature databases compared with the present state-of-the-art results.

Keywords

Offline signature verification Patch-wise Hierarchical convolutional neural network One-class classification 

Notes

References

  1. 1.
    ABA: Survey. https://www.aba.com/Press/Pages/DDAFraud012418.aspx (2017). Accessed 8 June 2018
  2. 2.
    Akbari, Y., Jalili, M.J., Sadri, J., Nouri, K., Siddiqi, I., Djeddi, C.: A novel database for automatic processing of persian handwritten bank checks. Pattern Recogn. 74, 253–265 (2018)CrossRefGoogle Scholar
  3. 3.
    Alaei, A., Pal, S., Pal, U., Blumenstein, M.: An efficient signature verification method based on an interval symbolic representation and a fuzzy similarity measure. IEEE Trans. Inf. Forensics Secur. 12(10), 2360–2372 (2017)CrossRefGoogle Scholar
  4. 4.
    Alvarez, G., Sheffer, B., Bryant, M.: Offline signature verification with convolutional neural networks. Tech. rep., Tech. rep., Stanford University, Stanford (2016)Google Scholar
  5. 5.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 7, 711–720 (1997)CrossRefGoogle Scholar
  6. 6.
    Bertolini, D., Oliveira, L.S., Justino, E., Sabourin, R.: Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers. Pattern Recogn. 43(1), 387–396 (2010)CrossRefGoogle Scholar
  7. 7.
    Bharathi, R., Shekar, B.: Off-line signature verification based on chain code histogram and support vector machine. In: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 2063–2068. IEEE (2013)Google Scholar
  8. 8.
    Chalechale, A., Mertins, A.: Line segment distribution of sketches for persian signature recognition. In: TENCON 2003. Conference on Convergent Technologies for the Asia-Pacific Region, vol. 1, pp. 11–15. IEEE (2003)Google Scholar
  9. 9.
    Chen, S., Srihari, S.: A new off-line signature verification method based on graph. In: 18th International Conference on Pattern Recognition, 2006. ICPR 2006. vol. 2, pp. 869–872. IEEE (2006)Google Scholar
  10. 10.
    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)
  11. 11.
    Diaz, M., Ferrer, M.A., Impedovo, D., Malik, M.I., Pirlo, G., Plamondon, R.: A perspective analysis of handwritten signature technology. ACM Comput. Surv. pp. 1–14 (2018)CrossRefGoogle Scholar
  12. 12.
    Foroozandeh, A., Akbari, Y., Jalili, M.J., Sadri, J.: A novel and practical system for verifying signatures on persian handwritten bank checks. Int. J. Pattern Recognit. Artif. Intell. 26(06), 1256014 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Foroozandeh, A., Akbari, Y., Jalili, M.J., Sadri, J.: Persian signature verification based on fractal dimension using testing hypothesis. In: 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 313–318. IEEE (2012)Google Scholar
  14. 14.
    Gilperez, A., Alonso-Fernandez, F., Pecharroman, S., Fierrez, J., Ortega-Garcia, J.: Off-line signature verification using contour features. In: 11th International Conference on Frontiers in Handwriting Recognition, Montreal, Quebec-Canada, August 19–21, 2008. CENPARMI, Concordia University (2008)Google Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    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, 219–232 (2018)CrossRefGoogle Scholar
  17. 17.
    Hafemann, L.G., Sabourin, R., Oliveira, L.S.: Offline handwritten signature verification-literature review. arXiv preprint arXiv:1507.07909 (2015)
  18. 18.
    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
  19. 19.
    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)CrossRefGoogle Scholar
  20. 20.
    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)CrossRefGoogle Scholar
  21. 21.
    Hu, J., Guo, Z., Fan, Z., Chen, Y.: Offline signature verification using local features and decision trees. Int. J. Pattern Recognit Artif Intell. 31(03), 1753,001 (2017)CrossRefGoogle Scholar
  22. 22.
    Impedovo, D., Pirlo, G.: Automatic signature verification: the state of the art. IEEE Trans. Syst. Man Cybernet Part C (Appl. Rev.) 38(5), 609–635 (2008)CrossRefGoogle Scholar
  23. 23.
    Impedovo, D., Pirlo, G., Russo, M.: Recent advances in offline signature identification. In: 2014 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 639–642. IEEE (2014)Google Scholar
  24. 24.
    Jayadevan, R., Kolhe, S.R., Patil, P.M., Pal, U.: Automatic processing of handwritten bank cheque images: a survey. Int. J. Doc. Anal. Recognit. (IJDAR) 15(4), 267–296 (2012)CrossRefGoogle Scholar
  25. 25.
    Kalera, M.K., Srihari, S., Xu, A.: Offline signature verification and identification using distance statistics. Int. J. Pattern Recognit. Artif Intell. 18(07), 1339–1360 (2004)CrossRefGoogle Scholar
  26. 26.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing System, pp. 1097–1105 (2012)Google Scholar
  27. 27.
    Leclerc, F., Plamondon, R.: Automatic signature verification: the state of the art 1989–1993. Int. J. Pattern Recognit Artif Intell. 8(03), 643–660 (1994)CrossRefGoogle Scholar
  28. 28.
    Nguyen, V., Blumenstein, M., Leedham, G.: Global features for the off-line signature verification problem. In: 10th International Conference on Document Analysis and Recognition, 2009. ICDAR’09, pp. 1300–1304. IEEE (2009)Google Scholar
  29. 29.
    Ooi, S.Y., Teoh, A.B.J., Pang, Y.H., Hiew, B.Y.: Image-based handwritten signature verification using hybrid methods of discrete radon transform, principal component analysis and probabilistic neural network. Appl. Soft Comput. 40, 274–282 (2016)CrossRefGoogle Scholar
  30. 30.
    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)CrossRefGoogle Scholar
  31. 31.
    Pal, S., Blumenstein, M., Pal, U.: Automatic off-line signature verification systems: a review. IJCA Proc. Int. Conf. Workshop Emerg. Trends Technol. (ICWET) 14, 20–27 (2011)Google Scholar
  32. 32.
    Perera, P., Patel, V.M.: Learning deep features for one-class classification. arXiv preprint arXiv:1801.05365 (2018)
  33. 33.
    Rantzsch, H., Yang, H., Meinel, C.: Signature embedding: Writer independent offline signature verification with deep metric learning. In: International Symposium on Visual Computing, pp. 616–625. Springer, Berlin (2016)CrossRefGoogle Scholar
  34. 34.
    Rivard, D., Granger, E., Sabourin, R.: Multi-feature extraction and selection in writer-independent off-line signature verification. Int. J. Doc. Anal. Recognit. 16, 83–103 (2013)CrossRefGoogle Scholar
  35. 35.
    Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)CrossRefGoogle Scholar
  36. 36.
    Shariatmadari, S., Al-maadeed, S., Akbari, Y., Rida, I., Emadi, S.: Off-line persian signature verification using wavelet-based fractal dimension and one-class gaussian process. In: 2011 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), Accepted. IEEE (2018)Google Scholar
  37. 37.
    Shariatmadari, S., Emadi, S., Akbari, Y.: Nonlinear dynamics tools for off-line signature verification using one-class gaussian process. Int. J. Pattern Recognit. Artif. Intell. 34(1) (2020)  https://doi.org/10.1142/S0218001420530018
  38. 38.
    Soleimani, A., Araabi, B.N., Fouladi, K.: Deep multitask metric learning for offline signature verification. Pattern Recogn. Lett. 80, 84–90 (2016)CrossRefGoogle Scholar
  39. 39.
    Soleimani, A., Fouladi, K., Araabi, B.N.: Utsig: a persian offline signature dataset. IET Biom. 6(1), 1–8 (2016)Google Scholar
  40. 40.
    Tax, D.M., Duin, R.P.: Support vector data description. Mach. Learn. 54(1), 45–66 (2004)CrossRefGoogle Scholar
  41. 41.
    Vargas, J., Ferrer, M., Travieso, C., Alonso, J.B.: Off-line signature verification based on grey level information using texture features. Pattern Recognit. 44(2), 375–385 (2011)CrossRefGoogle Scholar
  42. 42.
    Vo, Q.N., Kim, S.H., Yang, H.J., Lee, G.: Binarization of degraded document images based on hierarchical deep supervised network. Pattern Recognit. 74, 568–586 (2018)CrossRefGoogle Scholar
  43. 43.
    Zhang, Z., Liu, X., Cui, Y.: Multi-phase offline signature verification system using deep convolutional generative adversarial networks. In: 2016 9th International Symposium on Computational Intelligence and Design (ISCID), vol. 2, pp. 103–107. IEEE (2016)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Engineering, Yazd BranchIslamic Azad UniversityYazdIran
  2. 2.Department of Computer Science and EngineeringQatar UniversityDohaQatar

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