Making better biometric decisions with quality and cohort information: A case study in fingerprint verification
Making better biometric decisions with quality and cohort information: A case study in fingerprint verification
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DOI：
10.5281/zenodo.41648
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发表时间：
2009-08
期刊：
影响因子：
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通讯作者：
N. Poh;A. Merati;J. Kittler
中科院分区：
文献类型：
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作者：
N. Poh;A. Merati;J. Kittler
利用人脸和指纹等生物特征进行自动识别将在我们的日常生活中产生非常重要的影响。这个问题是具有挑战性的，因为生物特征可能受到对环境条件敏感的获取过程的影响（例如，照明）和用户交互。它已被证明，后处理的分类器输出，所谓的得分归一化，是一个重要的机制，以抵消上述问题。在文献中，两个主要的研究方向进行了探讨：队列标准化和基于质量的标准化。第一种方法依赖于一组竞争的队列模型，基本上是利用所得的队列分数。一个很好的例子是T范数。在第二种方法中，归一化基于从原始生物计量信号导出质量信息。我们建议通过逻辑回归将队列评分和信号衍生信息联合收割机结合起来。基于12个独立的指纹实验，我们的建议被发现是显着优于T-范数和最近提出的基于队列的归一化方法。
Automatically recognizing humans using their biometric traits such as face and fingerprint will have very important implications in our daily lives. This problem is challenging because biometric traits can be affected by the acquisition process which is sensitive to the environmental conditions (e.g., lighting) and the user interaction. It has been shown that post-processing the classifier output, so called score normalization, is an important mechanism to counteract the above problem. In the literature, two dominant research directions have been explored: cohort normalization and quality-based normalization. The first approach relies on a set of competing cohort models, essentially making use of the resultant cohort scores. A well-established example is the T-norm. In the second approach, the normalization is based on deriving the quality information from the raw biometric signal. We propose to combine both the cohort score- and signal-derived information via logistic regression. Based on 12 independent fingerprint experiments, our proposal is found to be significantly better than the T-norm and two recently proposed cohort-based normalization methods.