The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Biometrics
Likelihood Ratio-Based Biometric Score Fusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusion in Multibiometric Identification Systems: What about the Missing Data?
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Score normalization in multimodal biometric systems
Pattern Recognition
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
Multimodal biometric systems consolidate or fuse information from multiple biometric sources. They have been developed to overcome several limitations of each individual biometric system, such as sensitivity to noise, intra class invariability, data quality, non-universality and other factors. In this paper, we propose a general framework of multibiometric identification system based on fusion at matching score level using fuzzy set theory. The motivation for using fuzzy set theory is that it offers methods suited to treat (modeling, fusion,...) and take into account the information inherently uncertain and ambiguous. We note that our fusion system is based on face and iris modalities. Experimental results exhibit that the proposed method performance bring obvious improvement compared to unimodal biometric identification methods and classical combination approaches at score level fusion.