Nandakumar Karthik, Chen Yi, Dass Sarat C, Jain Anil K
Department of Computer Science and Engineering, Michigan State University, 3115 Engineering Building, East Lansing, MI 48824-1226, USA.
IEEE Trans Pattern Anal Mach Intell. 2008 Feb;30(2):342-7. doi: 10.1109/TPAMI.2007.70796.
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.
多生物特征识别系统融合来自不同来源的信息，以弥补单个匹配器在性能上的局限性。我们提出了一个基于似然比检验的匹配分数最优组合框架。真实匹配分数和冒名顶替者匹配分数的分布被建模为有限高斯混合模型。所提出的融合方法在处理以下方面具有通用性：（i）生物特征匹配分数分布中的离散值；（ii）匹配分数的任意尺度和分布；（iii）多个匹配器分数之间的相关性；（iv）多个生物特征源的样本质量。在三个多生物特征数据库上进行的实验表明，与基于分数变换和分类的常用分数融合技术相比，所提出的融合框架始终具有较高的性能。
