中文摘要：

通过多生物特征识别融合可以显著地改善系统的识别性能,在多生物特征识别中,匹配分数级融合最常用.现有的匹配分数级融合策略包括基于归一化的融合、基于密度的融合和基于分类器的融合.本文分析了这三种融合策略的优缺点,结合分数归一化和基于密度方法的优点,提出了一种新的基于信任度的融合策略.其中,信任度是以错误拒绝率和错误接受率为基础,既避免了直接求取某个匹配分数的后验概率,又能够刻画匹配分数的分布.将本文方法与几种有代表性的方法进行实验比较,结果表明,这种新融合模式可以有效地改进多生物特征识别系统的性能.

英文摘要：

Multibiometric systems are expected to be more accurate due to the presence of multiple evidences. Score level fusion is the most commonly used approach in multibiometrics. There are usually three kinds of techniques of score fusion： transformation-based, classifier-based and density-based. This paper firstly analyzes the advantages and disadvantages of the three types of algorithms. Then a novel confidence-based fusion technique is proposed which combines the advantages of transformation-based and density-based fusion strategies. The confidence is based on the false reject rate （FRR） and false accept rate （FAR） which can cultivate the distribution of the match scores while avoiding computing the posterior probability. The comparison between the new algorithm and the existing representative algorithms is conducted in experiments. The experimental results show that the new fusion scheme is robust for different multibiometric systems.