IEEE Trans Pattern Anal Mach Intell. 2012 Jan;34(1):3-18. doi: 10.1109/TPAMI.2011.102. Epub 2011 May 19.
This paper proposes a unified framework for quality-based fusion of multimodal biometrics. Quality-dependent fusion algorithms aim to dynamically combine several classifier (biometric expert) outputs as a function of automatically derived (biometric) sample quality. Quality measures used for this purpose quantify the degree of conformance of biometric samples to some predefined criteria known to influence the system performance. Designing a fusion classifier to take quality into consideration is difficult because quality measures cannot be used to distinguish genuine users from impostors, i.e., they are nondiscriminative yet still useful for classification. We propose a general Bayesian framework that can utilize the quality information effectively. We show that this framework encompasses several recently proposed quality-based fusion algorithms in the literature--Nandakumar et al., 2006; Poh et al., 2007; Kryszczuk and Drygajo, 2007; Kittler et al., 2007; Alonso-Fernandez, 2008; Maurer and Baker, 2007; Poh et al., 2010. Furthermore, thanks to the systematic study concluded herein, we also develop two alternative formulations of the problem, leading to more efficient implementation (with fewer parameters) and achieving performance comparable to, or better than, the state of the art. Last but not least, the framework also improves the understanding of the role of quality in multiple classifier combination.
本文提出了一种基于质量的多模态生物识别融合的统一框架。质量相关的融合算法旨在根据自动推导的（生物识别）样本质量，动态地组合多个分类器（生物识别专家）的输出。为此目的而使用的质量度量量化了生物识别样本符合某些预定义标准的程度，这些标准已知会影响系统性能。设计一个考虑质量的融合分类器是困难的，因为质量度量不能用于区分真实用户和伪造者，也就是说，它们是不可区分的，但对于分类仍然有用。我们提出了一个通用的贝叶斯框架，可以有效地利用质量信息。我们表明，这个框架包含了文献中最近提出的几种基于质量的融合算法——Nandakumar 等人，2006；Poh 等人，2007；Kryszczuk 和 Drygajo，2007；Kittler 等人，2007；Alonso-Fernandez，2008；Maurer 和 Baker，2007；Poh 等人，2010。此外，由于本文进行了系统的研究，我们还提出了该问题的两种替代公式化，从而实现了更有效的实现（参数更少），并达到了与最新技术相当或更好的性能。最后但同样重要的是，该框架还提高了对质量在多分类器组合中的作用的理解。
