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Multibiometric systems fuse information from different sources to compensate for the limitations in performance of individual matchers. We propose a framework for optimal combination of match scores that is based on the likelihood ratio test. The distributions of genuine and impostor match scores are modeled as finite Gaussian mixture model. The proposed fusion approach is general in its ability to handle (i) discrete values in biometric match score distributions, (ii) arbitrary scales and distributions of match scores, (iii) correlation between the scores of multiple matchers and (iv) sample quality of multiple biometric sources. Experiments on three multibiometric databases indicate that the proposed fusion framework achieves consistently high performance compared to commonly used score fusion techniques based on score transformation and classification.
Multibiometric systems, score level fusion, Neyman-Pearson theorem, likelihood ratio test, Gaussian mixture model, image quality
Sarat C. Dass, Anil Jain, Yi Chen, Karthik Nandakumar, "Likelihood Ratio-Based Biometric Score Fusion", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 342-347, February 2008, doi:10.1109/TPAMI.2007.70796
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