Hybrid rough neural network model for signature recognition

Mohamed Elhoseny, Amir Nabil, Aboul Ella Hassanien, Diego Oliva

Research output: Chapter in Book/Report/Conference proceedingChapter

13 Citations (Scopus)

Abstract

This chapter introduces an offline signature recognition technique using rough neural network and rough set. Rough neural network tries to find better recognition performance to classify the input offline signature images. Rough sets have provided an array of tools which turned out to be especially adequate for conceptualization, organization, classification, and analysis of various types of data, when dealing with inexact, uncertain, or vague knowledge. Also, rough sets discover hidden pattern and regularities in application. This new hybrid technique achieves good results, since the short rough neural network algorithm is neglected by the grid features technique, and then the advantages of both techniques are integrated.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Pages295-318
Number of pages24
DOIs
Publication statusPublished - 2018
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume730
ISSN (Print)1860-949X

Keywords

  • Neural network
  • Offline signature
  • Recognition

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Elhoseny, M., Nabil, A., Hassanien, A. E., & Oliva, D. (2018). Hybrid rough neural network model for signature recognition. In Studies in Computational Intelligence (pp. 295-318). (Studies in Computational Intelligence; Vol. 730). Springer Verlag. https://doi.org/10.1007/978-3-319-63754-9_14