Incorporating user specific normalization in multimodal biometric fusion system
2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)(2012)
摘要
The aim of this paper is to investigate the user-specific two-level fusion strategy in the context of multimodal biometrics. In this strategy, a client-specific score normalization procedure is applied firstly to each of the system outputs to be fused. Then, the resulting normalized outputs are fed into a common classifier. The logistic regression, non-confidence weighted sum and the likelihood ratio based on Gaussian mixture model are used as back-end classifiers. Three client-specific score normalization procedures are considered in this paper, i.e. Z-norm, F-norm and the Model-Specific Log-Likelihood Ratio MSLLR-norm. Our first findings based on 15 fusion experiments on the XM2VTS score database show that when the previous two-level fusion strategy is applied, the resulting fusion classifier outperforms the baseline classifiers significantly and a relative reduction of more than 50% in the equal error rate can be achieved. The second finding is that when using this two-level user-specific fusion strategy, the design of the final classifier is simplified and performance generalization of baseline classifiers is not straightforward. A great attention must be given to the choice of the combination normalization-back-end classifier.
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关键词
user specific normalization,multimodal biometric fusion system,user-specific two-level fusion strategy,client-specific score normalization procedure,logistic regression,nonconfidence weighted sum,Gaussian mixture model,Z-norm,F-norm,model-specific log-likelihood ratio,MSLLR-norm,fusion experiment,XM2VTS score database,fusion classifier,two-level user-specific fusion strategy,classifier design,performance generalization,baseline classifier,combination normalization-back-end classifier
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