Signature recognition using neural network probabilistic
(1) * Heri Nurdiyanto Mail (STMIK Dharma Wacana, Indonesia)
(2) Hermanto Hermanto Mail (STMIK Dharma Wacana, Indonesia)
*corresponding author

Abstract


The signature of each person is different and has unique characteristics. Thus, this paper discusses the development of a personal identification system based on it is unique digital signature. The process of preprocessing used gray scale method, while Shannon Entropy and Probabilistic Neural Network are used respectively for feature extraction and identification. This study uses five signature types with five signatures in every type. While the test results compared to actual data compared to real data, the proposed system performance was only 40%.

Keywords


probabilistic neural network; entropy; identification of signatures

   

DOI

https://doi.org/10.26555/ijain.v2i1.53 		      

Article metrics

Abstract views : 2194 | PDF views : 338

   

Cite

How to cite item
   

Full Text

Download

References


D. F. Specht, “Probabilistic Neural Networks,” Neural Netw., vol. 3, no. 1, pp. 109–118, Jan. 1990.

S. G. Wu, F. S. Bao, E. Y. Xu, Y. X. Wang, Y. F. Chang, and Q. L. Xiang, “A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network,” in 2007 IEEE International Symposium on Signal Processing and Information Technology, 2007, pp. 11–16.

A. K. Jain, F. D. Griess, and S. D. Connell, “On-line signature verification,” Pattern Recognit., vol. 35, no. 12, pp. 2963–2972, 2002.

D. F. Specht. "Probabilistic Neural Networks". Journal of Neural Networks, vol.3,pp.,109-118,1990.

D. K. Sharma, C.S. Rai, andL. Gaur. “Linear versus nonlinear algorithms for feature extraction and image compression,” Proceedings of the Academy of Information and Management Sciences, Las Vegas (US),2011, pp. 21-26.

P. Liu, Z. Zeng, and Z. Wang, 2016, Multistability analysis of a general class of recurrent neural networks with non-monotonic activation functions and time-varying delays, Neural Networks Journal pp. 117-127

P. S. Patil, S. R. Kolhe, R. V Patil, and P. M. Patil, “Article: Performance Evaluation in Iris Recognition and CBIR System based on Phase Congruency,” Int. J. Comput. Appl., vol. 47, no. 14, pp. 13–18, Jun. 2012.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification (2nd Edition). Wiley-Interscience, 2000




Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
   andri.pranolo.id@ieee.org (publication issues)

View IJAIN Stats

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0