演化多模态生物特征系统中的缺失分数研究
On Missing Scores in Evolving Multibiometric Systems
AI总结：
本研究针对演化多模态生物特征系统中缺失分数问题，提出结合多种分数插补与求和融合的方法，在缺失比例高达90%时仍提升验证与识别精度，其中K近邻迭代插补效果最优。
摘要：
使用多种模态（例如人脸和指纹）或多种算法（例如三个人脸比较器）已被证明能够提高运行中的生物特征识别系统的识别精度。随着时间的推移，生物特征系统可能会演化，增加新的模态、淘汰旧的模态，或与其他生物特征系统合并。这可能导致出现与输入探针集相对应的缺失分数的场景。以往关于该主题的研究要么只关注验证任务，要么只关注识别任务，但并未同时关注两者。此外，所考虑的缺失数据比例低于50%。在本工作中，我们研究了缺失分数数据对验证和识别任务的影响。我们表明，应用各种分数插补方法并结合简单的求和融合，即使在缺失分数比例增加到90%的情况下，也能提高识别精度。实验表明，分数插补后的融合优于无插补的融合。具体而言，使用K近邻的迭代插补在验证和识别任务中均始终优于其他插补方法，无论缺失分数的数量如何，并且提供的插补值与真实完整数据集一致。
英文摘要：
The use of multiple modalities (e.g., face and fingerprint) or multiple algorithms (e.g., three face comparators) has shown to improve the recognition accuracy of an operational biometric system. Over time a biometric system may evolve to add new modalities, retire old modalities, or be merged with other biometric systems. This can lead to scenarios where there are missing scores corresponding to the input probe set. Previous work on this topic has focused on either the verification or identification tasks, but not both. Further, the proportion of missing data considered has been less than 50%. In this work, we study the impact of missing score data for both the verification and identification tasks. We show that the application of various score imputation methods along with simple sum fusion can improve recognition accuracy, even when the proportion of missing scores increases to 90%. Experiments show that fusion after score imputation outperforms fusion with no imputation. Specifically, iterative imputation with K nearest neighbors consistently surpasses other imputation methods in both the verification and identification tasks, regardless of the amount of scores missing, and provides imputed values that are consistent with the ground truth complete dataset.
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