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Effect of Moment Invariants on Signature Recognition Rate by using Fuzzy Min-Max Neural Networks

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IJCA Proceedings on National Conference on Advances in Communication and Computing
© 2014 by IJCA Journal
NCACC 2014 - Number 1
Year of Publication: 2014
Authors:
Jayesh Rane
Sagar More

Jayesh Rane and Sagar More. Article: Effect of Moment Invariants on Signature Recognition Rate by using Fuzzy Min-Max Neural Networks. IJCA Proceedings on National Conference on Advances in Communication and Computing NCACC(1):21-24, December 2014. Full text available. BibTeX

@article{key:article,
	author = {Jayesh Rane and Sagar More},
	title = {Article: Effect of Moment Invariants on Signature Recognition Rate by using Fuzzy Min-Max Neural Networks},
	journal = {IJCA Proceedings on National Conference on Advances in Communication and Computing},
	year = {2014},
	volume = {NCACC},
	number = {1},
	pages = {21-24},
	month = {December},
	note = {Full text available}
}

Abstract

This paper presents a method of recognition of signatures by Fuzzy Min-Max Neural Networks and analyses the effect of moment invariants on signature recognition by comparing the accuracy of recognition. In addition, database is also tested by fuzzy min-max neural networks for recognition of signatures resulting more accurate results. Image processing and fuzzy neural network toolboxes are used in person identification system provided by MATLAB. For the identification of signatures database is created for five persons with the thirty times repetitions. These signatures are preprocessed by scanning the images and then converting them to standard binary images. The features are selected and extracted which gives the information about the structure of signature. This paper also investigates the performance of the system by using fuzzy min max neural networks classifier.

References

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