CONF Poh:2002:nnsp/IDIAP A Multi-sample Multi-source Model for Biometric Authentication Poh, Norman Bengio, Samy Korczak, Jerzy EXTERNAL https://publications.idiap.ch/attachments/reports/2002/rr02-14.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/poh02 Related documents IEEE International Workshop on Neural Networks for Signal Processing (NNSP) 2002 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 samples obtained from multiple biometric sources at the score level. By using the average operator, both the theoretical and empirical results 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. REPORT Poh02/IDIAP A Multi-sample Multi-source Model for Biometric Authentication Poh, Norman Bengio, Samy Korczak, Jerzy EXTERNAL https://publications.idiap.ch/attachments/reports/2002/rr02-14.pdf PUBLIC Idiap-RR-14-2002 2002 IDIAP 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 samples obtained from multiple biometric sources at the score level. By using the average operator, both the theoretical and empirical results 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.