A MULTI-SAMPLE MULTI-SOURCE MODEL FO R B I 0 M ET R I C AUT H ENT I CAT I 0 N
In this study, two techniques that can improve the authentication process are examined: (i) multiple samples and (ii) multiple biometric sources. We propose the fusion of multiple sam- ples obtained from multiple biometric sources at the score level. By using the average operator, both the theoretical and empirical re- sults show that integrating as many samples and as many biometric sources as possible can improve the overall reliability of the system. This strategy is called multi-sample multi-source approach. This strategy was tested on a real-life database using neural networks trained in one-versus-all configuration.