Norwegian Biometric Laboratory, Gjøvik University College, Teknologivegen 22, N2802 Gjøvik, Norway.
Sensors (Basel). 2012;12(5):5246-72. doi: 10.3390/s120505246. Epub 2012 Apr 26.
In a biometric authentication system using protected templates, a pseudonymous identifier is the part of a protected template that can be directly compared. Each compared pair of pseudonymous identifiers results in a decision testing whether both identifiers are derived from the same biometric characteristic. Compared to an unprotected system, most existing biometric template protection methods cause to a certain extent degradation in biometric performance. Fusion is therefore a promising way to enhance the biometric performance in template-protected biometric systems. Compared to feature level fusion and score level fusion, decision level fusion has not only the least fusion complexity, but also the maximum interoperability across different biometric features, template protection and recognition algorithms, templates formats, and comparison score rules. However, performance improvement via decision level fusion is not obvious. It is influenced by both the dependency and the performance gap among the conducted tests for fusion. We investigate in this paper several fusion scenarios (multi-sample, multi-instance, multi-sensor, multi-algorithm, and their combinations) on the binary decision level, and evaluate their biometric performance and fusion efficiency on a multi-sensor fingerprint database with 71,994 samples.
在使用受保护模板的生物特征认证系统中，化名标识符是受保护模板中可直接比较的部分。每对比较的化名标识符都会产生一个决策测试，以确定两个标识符是否都来自相同的生物特征。与非受保护系统相比，大多数现有的生物特征模板保护方法都会在一定程度上降低生物特征性能。因此，融合是增强受保护生物特征系统中生物特征性能的一种很有前途的方法。与特征级融合和分数级融合相比，决策级融合不仅具有最小的融合复杂性，而且在不同的生物特征、模板保护和识别算法、模板格式和比较分数规则方面具有最大的互操作性。然而，通过决策级融合提高性能并不明显。它受到融合所进行的测试的依赖性和性能差距的影响。本文在二进制决策级上研究了几种融合方案（多样本、多实例、多传感器、多算法及其组合），并在一个具有 71994 个样本的多传感器指纹数据库上评估了它们的生物特征性能和融合效率。
