DocumentCode :
319638
Title :
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
Link To Document :
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