A study on signature verification using a new approach to genetic based machine learning

Author

Yang, Xu ; Furuhashi, Takeshi ; OBATA, Kenzo ; Uchikawa, Yoshiki

Author_Institution

Dept. of Inf. Electron., Nagoya Univ., Japan

Volume

5

fYear

1995

fDate

22-25 Oct 1995

Firstpage

4383

Abstract

This paper presents a new method to find best features for signature verification. The new method uses a new coding method, a new crossover method, and a new GA method with a local improvement mechanism proposed by the authors. The new coding method is effective to absorb the intra-personal variability among true signatures. The new crossover method determines the number of partial curves chosen for the signature verification. The new GA approach is very efficient in improving the local portions of chromosomes. Experiments are done to show the effectiveness of the new method

Keywords

encoding; feature extraction; fuzzy neural nets; genetic algorithms; handwriting recognition; learning (artificial intelligence); coding method; crossover method; genetic based machine learning; intra-personal variability; local improvement mechanism; signature verification; Automation; Biological cells; Cranes; Error analysis; Fuzzy systems; Genetics; Handwriting recognition; Machine learning; Prototypes; Security;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-2559-1

Type

conf

DOI

10.1109/ICSMC.1995.538483

Filename

538483