Signature verification using shape descriptors and multiple neural networks

Author

Dehghan, Mehdi ; Faez, Karim ; Fathi, Mahmood

Author_Institution

Dept. of Electr. Eng., Amirkabir Univ. of Technol., Tehran, Iran

Volume

1

fYear

1997

fDate

4-4 Dec. 1997

Firstpage

415

Abstract

This paper presents an off-line signature verification system. The verification is based on signature shape descriptors such as the skeleton, upper and lower envelopes, and the high pressure region of the signatures. Multiple multilayer perceptron neural network modules cooperating in taking a verification decision via a fuzzy integral voter are used. This method is capable of verifying simple and skilled forgeries with a good performance.

Keywords

feature extraction; fuzzy set theory; handwriting recognition; image classification; learning (artificial intelligence); multilayer perceptrons; feature extraction; forgeries; fuzzy integral voter; high pressure region; lower envelope; multilayer perceptron neural network modules; off-line signature verification system; performance; shape descriptors; signature classification; skeleton; upper envelope; Authorization; Forgery; Handwriting recognition; Neural networks; Noise shaping; Pattern classification; Prototypes; Security; Shape; Skeleton;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON '97. IEEE Region 10 Annual Conference. Speech and Image Technologies for Computing and Telecommunications., Proceedings of IEEE

Conference_Location

Brisbane, Qld., Australia

Print_ISBN

0-7803-4365-4

Type

conf

DOI

10.1109/TENCON.1997.647344

Filename

647344