Title :
An integration method of multi-modal biometrics using supervised pareto learning self organizing maps
Author :
Dozono, Hiroshi ; Nakakuni, Masanori
Author_Institution :
Fac. of Sci. & Eng., Saga Univ., Saga
Abstract :
This paper proposes a method for the integration of multi-modal biometrics. As the conventional authentication method, password system is mostly used. But, password mechanism has many issues. In order to solve the problems, biometric authentication methods are often used. But, the authentication method using biological characteristics, such as fingerprint, also has some problems. In this paper, we propose a authentication method using multi-modal behavior biometrics sampled from keystroke timings and handwritten patterns. And supervised Pareto learning self organizing maps which integrate the multi-modal vectors is proposed. The performance of this method is confirmed by the authentication experiments.
Keywords :
Pareto optimisation; handwriting recognition; image sampling; learning (artificial intelligence); message authentication; self-organising feature maps; vectors; authentication method; behavior biometrics sampling; biological characteristics; handwritten pattern; keystroke timing; multimodal biometrics integration method; multimodal vector; password system; supervised Pareto learning self organizing map; Authentication; Biology computing; Biometrics; Computer hacking; Computer security; Fingerprint recognition; Keyboards; Personal communication networks; Self organizing feature maps; Timing;
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2008.4633855