As biometric technology is increasingly deployed, it will be common to replace parts of operational systems with newer designs. The cost and inconvenience of reacquiring enrolled users when a new vendor solution is incorporated makes this approach difficult and many applications will require to deal with information from different sources regularly. These interoperability problems can dramatically affect the performance of biometric systems and thus, they need to be overcome. Here, we describe and evaluate the ATVS-UAM fusion approach submitted to the quality-based evaluation of the 2007 BioSecure Multimodal Evaluation Campaign, whose aim was to compare fusion algorithms when biometric signals were generated using several biometric devices in mismatched conditions. Quality measures from the raw biometric data are available to allow system adjustment to changing quality conditions due to device changes. This system adjustment is referred to as quality-based conditional processing. The proposed fusion approach is based on linear logistic regression, in which fused scores tend to be log-likelihood-ratios. This allows the easy and efficient combination of matching scores from different devices assuming low dependence among modalities. In our system, quality information is used to switch between different system modules depending on the data source (the sensor in our case) and to reject channels with low quality data during the fusion. We compare our fusion approach to a set of rule-based fusion schemes over normalized scores. Results show that the proposed approach outperforms all the rule-based fusion schemes. We also show that with the quality-based channel rejection scheme, an overall improvement of 25% in the equal error rate is obtained.
翻译:随着生物鉴别技术的日益应用,通常的做法是用较新的设计来取代部分操作系统,在采用新的供应商解决方案时,重新获取注册用户的费用和不便使得这种方法难以采用,许多应用程序都需要定期处理不同来源的信息。这些互操作性问题会严重影响生物鉴别系统的性能,因此需要克服这些问题。在这里,我们描述并评价向2007年生物保密多式评价运动质量评估提交的ATVS-UAM混合法,其目的是比较在不匹配的条件下使用若干生物鉴别装置生成生物鉴别信号时的聚合算法。原始生物鉴别数据的质量措施可用于系统调整,以适应因设备改变而改变的质量条件。这种系统调整被称为基于质量的有条件处理。拟议的聚合方法基于线性后勤回归,其中混成的分数往往与行距相似。这样可以方便和有效地结合基于不同装置的得分,假设不同方式之间依赖性较低。在我们系统中,质量信息的质量信息用于不同系统模块之间的转换,取决于数据源的总体质量方法(我们标定的传感器比率)和我们标定的递增率方法。