Skip to main content
SpringerLink
Log in

Recurrent Binary Patterns and CNNs for Offline Signature Verification

  • Conference paper
  • First Online:
Proceedings of the Future Technologies Conference (FTC) 2019 (FTC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1070))

Included in the following conference series:

Abstract

Signature representations that are extracted by convolutional neural networks (CNN) can achieve low error rates. However, a trade-off exists between such models’ complexities and hand-crafted features’ slightly higher error rates. A novel writer-dependent (WD) recurrent binary pattern (RBP) network, and a novel signer identification CNN is proposed. RBP network is a recurrent neural network (RNN) to learn the sequential relation between binary pattern histograms over image windows. A novel histogram selection method is introduced to remove the stop-word codes. Dimensionality is reduced by more than 25% while improving the results. This work is the first to combine binary patterns and RNNs for static signature verification. Several test sets, derived from large-scale and popular databases (GPDS-960 and GPDS-Synthetic-10000) are used. Without training any global classifier, RBP network provides competitive equal error rates (EER). The proposed architectures are compared and integrated with other recent CNN models. Score-level integration of WD classifiers trained with different representations are investigated. Cross-validation tests demonstrate the EERs reduced compared to the best single classifier. A state-of-the-art EER of 1.11% is reported with a global decision threshold (0.57% EER with user-based thresholds) on GPDS-160 database.

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 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
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. Alonso-Fernandez, F., Fairhurst, M.C., Fierrez, J., Ortega-Garcia, J.: Impact of signature legibility and signature type in off-line signature verification. In: 2007 Biometrics Symposium, pp. 1–6 (2007)

    Google Scholar 

  2. Galbally, J., Gomez-Barrero, M., Ross, A.: Accuracy evaluation of handwritten signature verification: rethinking the random-skilled forgeries dichotomy. In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 302–310 (2017)

    Google Scholar 

  3. Graves, A., Schmidhuber, J.: Offline handwriting recognition with multidimensional recurrent neural networks. In: Koller, D., Schuurmans, D., Bengio, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems 21, pp. 545–552. Curran Associates Inc., Red Hook (2009)

    Google Scholar 

  4. Byeon, W., Breuel, T.M., Raue, F., Liwicki, M.: Scene labeling with LSTM recurrent neural networks. In: CVPR, pp. 3547–3555. IEEE Computer Society (2015)

    Google Scholar 

  5. Visin, F., Kastner, K., Cho, K., Matteucci, M., Courville, A., Bengio, Y.: ReNet: a recurrent neural network based alternative to convolutional networks. arXiv preprint arXiv:1505.00393 (2015)

  6. Bluche, T., Messina, R.: Faster segmentation-free handwritten Chinese text recognition with character decompositions. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 530–535 (2016)

    Google Scholar 

  7. Shkarupa, Y., Mencis, R., Sabatelli, M.: Offline handwriting recognition using LSTM recurrent neural networks. In: The 28th Benelux Conference on Artificial Intelligence, Amsterdam (NL), 10–11 November 2016, vol. 1, p. 88 (2016)

    Google Scholar 

  8. Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Ortega-Garcia, J.: Biometric signature verification using recurrent neural networks. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 01, pp. 652–657 (2017)

    Google Scholar 

  9. Fierrez, J., Galbally, J., Ortega-Garcia, J., Freire, M., Alonso-Fernandez, F., Ramos, D., Toledano, D., González, J., Siguenza, J., Garrido-Salas, J., Anguiano, E., González, G., Ribalda, R., Ortega, J., Cardeñoso, V., Viloria, A., Vivaracho, C., Moro, Q., Faúndez, M., Igarza, J., Sanchez, J., Hernaez, J., Orrite, C., Martinez, F., Gracia, J.: BiosecurID: a multimodal biometric database. Pattern Anal. Appl. 13(2), 235–246 (2010)

    Article  MathSciNet  Google Scholar 

  10. Jain, M., Mathew, M., Jawahar, C.V.: Unconstrained OCR for Urdu using Deep CNN-RNN hybrid networks. In: 4th Asian Conference on Pattern Recognition (ACPR 2017), Nanjing, China, p. 6 (2017)

    Google Scholar 

  11. Dutta, K., Krishnan, P., Mathew, M., Jawahar, C.: Improving CNN-RNN hybrid networks for handwriting recognition. In: The 16th International Conference on Frontiers in Handwriting Recognition, Niagara Falls, USA (2018)

    Google Scholar 

  12. Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Ortega-Garcia, J.: Exploring recurrent neural networks for on-line handwritten signature biometrics. IEEE Access 6, 5128–5138 (2018)

    Article  Google Scholar 

  13. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  14. Benrachou, D.E., dos Santos, F.N., Boulebtateche, B., Bensaoula, S.: Online vision-based eye detection: LBP/SVM vs LBP/LSTM-RNN. In: CONTROLO 2014 Proceedings of the 11th Portuguese Conference on Automatic Control, pp. 659–668 (2014)

    Google Scholar 

  15. Juefei-Xu, F., Boddeti, V.N., Savvides, M.: Local binary convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19–28. IEEE (2017)

    Google Scholar 

  16. Zhang, H., Qu, Z., Yuan, L., Li, G.: A face recognition method based on LBP feature for CNN. In: 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 544–547 (2017)

    Google Scholar 

  17. Anwer, R.M., Khan, F.S., van de Weijer, J., Molinier, M., Laaksonen, J.: Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification. ISPRS J. Photogram. Remote Sens. 138, 74–85 (2018)

    Article  Google Scholar 

  18. Díaz, M., Ferrer, M.A., Impedovo, D., Malik, M.I., Pirlo, G., Plamondon, R.: A perspective analysis of handwritten signature technology. ACM Comput. Surv. 51(6), 117:1–117:39 (2019)

    Article  Google Scholar 

  19. Ferrer, M.A., Alonso, J.B., Travieso, C.M.: Offline geometric parameters for automatic signature verification using fixed-point arithmetic. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 993–997 (2005)

    Article  Google Scholar 

  20. Yılmaz, M.B., Yanıkoğlu, B.: Score level fusion of classifiers in off-line signature verification. Inf. Fusion 32(Part B), 109–119 (2016). SI Information Fusion in Biometrics

    Article  Google Scholar 

  21. Hafemann, L.G., Sabourin, R., Oliveira, L.S.: Learning features for offline handwritten signature verification using deep convolutional neural networks. Pattern Recogn. 70(C), 163–176 (2017)

    Google Scholar 

  22. 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)

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Yılmaz, M.B., Öztürk, K.: Hybrid user-independent and user-dependent offline signature verification with a two-channel CNN. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2018)

    Google Scholar 

  25. Zois, E.N., Alexandridis, A., Economou, G.: Writer independent offline signature verification based on asymmetric pixel relations and unrelated training-testing datasets. Expert Syst. Appl. 125, 14–32 (2019)

    Article  Google Scholar 

  26. Kalera, M.K., Srihari, S.N., Xu, A.: Offline signature verification and identification using distance statistics. IJPRAI 18(7), 1339–1360 (2004)

    Google Scholar 

  27. Ortega-Garcia, J., Fierrez-Aguilar, J., Simon, D., Gonzalez, J., Faundez-Zanuy, M., Espinosa, V., Satue, A., Hernaez, I., Igarza, J., Vivaracho, C., Escudero, D., Moro, Q.: MCYT baseline corpus: a bimodal biometric database. IEE Proc. Vis. Image Sig. Process. 150(6), 395–401 (2003)

    Article  Google Scholar 

  28. Alewijnse, L.: Analysis of signature complexity. Master thesis. University of Amsterdam (2008)

    Google Scholar 

  29. Liwicki, M., van den Heuvel, C.E., Found, B., Malik, M.I.: Forensic signature verification competition 4NSigComp2010 - detection of simulated and disguised signatures. In: 2010 12th International Conference on Frontiers in Handwriting Recognition, pp. 715–720 (2010)

    Google Scholar 

  30. Yılmaz, M.B.: Offline signature verification with user-based and global classifiers of local features. Ph.D. thesis, Sabancı University (2015)

    Google Scholar 

  31. Ferrer, M.A., Diaz, M., Carmona-Duarte, C., Morales, A.: A behavioral handwriting model for static and dynamic signature synthesis. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1041–1053 (2017)

    Article  Google Scholar 

  32. Ferrer, M.A., Diaz-Cabrera, M., Morales, A.: Static signature synthesis: a neuromotor inspired approach for biometrics. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 667–680 (2015)

    Article  Google Scholar 

  33. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015) (2015)

    Google Scholar 

  34. Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: 4th International Conference on Learning Representations, Conference Track Proceedings, ICLR 2016, San Juan, Puerto Rico, 2–4 May 2016 (2016)

    Google Scholar 

  35. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27–30 June 2016, pp. 770–778 (2016)

    Google Scholar 

  36. Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269 (2017)

    Google Scholar 

  37. Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., Chen, L.: MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18–22 June 2018, pp. 4510–4520 (2018)

    Google Scholar 

Download references

Acknowledgments

This work was supported by The Scientific Research Projects Coordination Unit of Akdeniz University, project number: 3780.

Author information

Authors and Affiliations

  1. Akdeniz University, Antalya, Turkey

    Mustafa Berkay Yılmaz

  2. Alanya Alaaddin Keykubat University, Antalya, Turkey

    Kağan Öztürk

Authors
  1. Mustafa Berkay Yılmaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kağan Öztürk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mustafa Berkay Yılmaz .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yılmaz, M.B., Öztürk, K. (2020). Recurrent Binary Patterns and CNNs for Offline Signature Verification. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2019. FTC 2019. Advances in Intelligent Systems and Computing, vol 1070. Springer, Cham. https://doi.org/10.1007/978-3-030-32523-7_29

Download citation

Publish with us

Policies and ethics

Search

Navigation