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Signature Recognition Using Backpropagation Neural Network

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13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018 (ICAFS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 896))

Abstract

Imitation or the fake signatures is the global fraud that cause the waste of financial sources, time and human effort. For this reason, signature recognition is the most widely used biometrics system for security and personal identification. Signatures are the most complex human patterns which are used to identify and approve the authorized persons. They can be varied according to the paper and pen influences, and human psychology and characteristics at the signature moment. Therefore, effective recognition of signatures is required in order to minimize the fraud. The usage of neural networks in biometrics, yet signature recognition, provides more steady and accurate identification thus authorization of person. This paper presents the preliminary results of developed offline signature recognition system using backpropagation neural network. Signature database is created by collecting the multiple signatures of 27 persons and the accuracy of the system is tested under artificially created conditions. System achieved 86% of highest recognition rate.

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References

  1. Arora, A., Choubey, A.S.: Offline signature and recognition using neural network. Int. J. Sci. Res. 2(8), 196–200 (2013)

    Google Scholar 

  2. Prabhakar, S., Pankanti, S., Jain, A.K.: Biometric recognition: security and privacy concerns. IEEE Secur. Priv. 99(2), 33–42 (2003)

    Article  Google Scholar 

  3. Maiorana, E., Campisi, P., Fierrez, J., Ortega-Garcia, J., Neri, A.: Cancelable templates for sequence-based biometrics with application to on-line signature recognition. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum. 40(3), 525–538 (2010)

    Article  Google Scholar 

  4. Mlaba, A., Gwetu, M., Viriri, S.: Online signature verification using hybrid transform features. In: 2018 Conference on Information Communications Technology and Society (ICTAS), pp. 1–5. IEEE, South Africa (2018)

    Google Scholar 

  5. Kumar, J., Singh, A.K.: Workload prediction in cloud using artificial neural network and adaptive differential evolution. Future Gener. Comput. Syst. 81, 41–52 (2018)

    Article  Google Scholar 

  6. Wu, K., Zhong, Y., Wang, X., Sun, W.: A novel approach to subpixel land-cover change detection based on a supervised back-propagation neural network for remotely sensed images with different resolutions. IEEE Geosci. Remote Sens. Lett. 14(10), 1750–1754 (2017)

    Article  Google Scholar 

  7. Sekeroglu, B., Inan, Y.: Leaves recognition using neural networks. Procedia Comput. Sci. 102(C), 578–582 (2016)

    Article  Google Scholar 

  8. Adali, T., Sekeroglu, B.: Analysis of microRNAs by neural network for early detection of cancer. Procedia Technol. 1, 449–452 (2012)

    Article  Google Scholar 

  9. Khashman, A., Sekeroglu, B.: Document image binarisation using a supervised neural network. Int. J. Neural Syst. 18, 405–418 (2008)

    Article  Google Scholar 

  10. Nasser, A.T., Dogru, N.: Signature recognition by using SIFT and SURF with SVM basic on RBF for voting online. In: 2017 International Conference on Engineering and Technology (ICET), pp. 1–5. IEEE, Antalya (2017)

    Google Scholar 

  11. Patil, P., Almeida, B., Chettiar, N., Babu, J.: Offline signature recognition system using histogram of oriented gradients. In: 2017 International Conference on Advances in Computing, Communication and Control (ICAC3), pp. 1–5. IEEE, Mumbai (2017)

    Google Scholar 

  12. Hiryanto, L., Yohannis, A.R., Handhayani, T.: Hand signature and handwriting recognition as identification of the writer using gray level cooccurrence matrix and bootstrap. In: 2017 Intelligent Systems Conference (IntelliSys), pp. 1103–1110. IEEE, London (2017)

    Google Scholar 

  13. Demir, Z., Çikoğlu, S., Temurtaş, F., Yumuşak, N.: Signature recognition using a neural network model. SAU Fen Bilimleri Enstitüsü Dergisi 7(2) (2003)

    Google Scholar 

  14. Karoni, A., Daya, B., Bahlak, S.: Offline signature using neural networks approach. Procedia Comput. Sci. 3, 155–161 (2011)

    Article  Google Scholar 

  15. Bhatia, M.: Hand-written signature verification using neural network. Int. J. Appl. Inf. Syst. (IJAIS) 1(2), 44–49 (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Near East University, Nicosia, Mersin 10, Turkey

    Yucel Inan

  2. Department of Information Systems Engineering, Near East University, Nicosia, Mersin 10, Turkey

    Boran Sekeroglu

Authors
  1. Yucel Inan
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  2. Boran Sekeroglu
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Corresponding author

Correspondence to Yucel Inan .

Editor information

Editors and Affiliations

  1. Joint MBA Program, Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    Rafik A. Aliev

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  4. Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA

    Mo. Jamshidi

  5. Department of Mechatronics, Near East University, Nicosia TRNC, Turkey

    Fahreddin M. Sadikoglu

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Inan, Y., Sekeroglu, B. (2019). Signature Recognition Using Backpropagation Neural Network. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F. (eds) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing, vol 896. Springer, Cham. https://doi.org/10.1007/978-3-030-04164-9_35

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