9th International Science Congress (ISC-2019).  International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Neural Network Based Offline Signature Recognition and Verification System

Author Affiliations

  • 1 Department of Electrical Engineering, Jabalpur Engineering College Jabalpur, MP, INDIA

Res. J. Engineering Sci., Volume 2, Issue (2), Pages 11-15, February,26 (2013)

Abstract

Handwritten signatures are the most natural way of authenticating a personís identity. An offline signature verification system generally consists of four components: data acquisition, pre- processing, feature extraction, recognition and verification. This paper presents a method for verifying handwritten signature by using NN architecture. In proposed methods the multi-layer perceptron (MLP), modular neural networks with generalized feed-forward networks and Self Organizing Map groups (SOM) neural network with competitive learning will be considered. Self Organizing Map groups the input data into clusters which are commonly used for unsupervised training. After recognition and verification of input data FRR, FAR and TER is calculated.

References

  1. Syed Khaleel Ahmed, Tan Yu Jian, and Jamaluddin Omar, On-line signature verification: A prototype using pressure and position, IEEE Student Conference on Research & Development, UTM Skudai, Johor, (2008)
  2. Kresimir Delac and Mislav Grgic, A survey of biometric recognition methods, 46th International Symposium in Marine, ELMAR-2004, 16-18 June, Zadar, Croatia, (2004)
  3. Modi S.K. and Elliott S.J., Keystroke dynamics verification using a spontaneously generated password, In Proceedings of the 40th IEEE International Carnahan Conference on Security Technology, Lexington Kentucky, (2006)
  4. Lejtman D.Z., On-line handwritten signature verification using wavelets and back-propagation neural networks, Proceedings of the Sixth International Conference on Document Analysis and Recognition, 992, (2001)
  5. Vance Faber, Clustering And the Continuous K-means Algorithm, Los Alamos Science, Number 22, (1994)
  6. Wee C.Y. and Paramesran R., Image Sharpness measure using Eigenvalues, In Proceeding of IEEE 9th international conference on Signal processing, ICSP2008, Beijing, 840-843 (2008)
  7. Ranade A., Mahabalarao S.S. and Kale S., A variation on SVD based image compression, J. of Image and Vision Computing, 25, 771-777 (2007)
  8. Sun T.H., Liu C.S. and Tien F.C., Invariant 2D objects recognition using eigen values of covariance Matrices, re-sampling and autocorrelation, Expert System with Applications, 35, 1966-1977 (2008)
  9. Xu Y., Song F., Feng G. and Zhao Y., A novel local preserving projection scheme for use with Recognition, Expert Systems with Applications, 37(9), 6718-6721 (2010)
  10. Andrzej Pacut and Adam Czajka, Recognition of human signatures, Proceedings of the International Joint Conference on Neural Networks Washington, DC, USA (2001)
  11. Phua K., Chen J., Huy Dat T. and Shue L., Heart, Sound as a biometric, Pattern Recognition, 41(3), 906919 (2008)
  12. Shivanandam S.N., Sumathi S., Deepa S.N., Introduction To Neural Networks Using Matlab 6.0, TMH. 3. S.K. Modi and S.J. Elliott, Keystroke dynamics verification using a spontaneously generated password, In Proceedings of the 40th IEEE International Carnahan Conference on Security Technology, Lexington Kentucky, (2006)
  13. Kishan Mehrotra, Chilukuri K. Moham Sanjay Ranka, Elements Artificial Neural Networks, Penram International Publishing (India) Pvt. Ltd.
  14. Plamondon R. and Leclerc F., Automatic signature verification: the state of the art 1989-1993, International Journal of Pattern Recognition and Artificial Intelligence, 8(3), 643-660 (1994)
  15. Plamondon R. and Srihari S.N., On-line and off-line handwriting recognition: A comprehensive survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 63-84 (2000)