插补与否：多生物特征融合的建议
To Impute or Not: Recommendations for Multibiometric Fusion
AI总结：
该研究针对多生物特征融合中的分数缺失问题，在三个多模态数据集上评估多种插补方法，明确了插补的必要性、类别平衡的关键作用及不同插补方法的适用场景，为多生物特征融合提供实践建议。
摘要：
通过融合组合来自不同生物特征系统的匹配分数是一种提升识别准确率的成熟方法。然而，缺失的分数会降低性能，还会限制可应用的融合技术范围。插补是多生物特征系统中用于替换缺失数据的一项颇具前景的技术。本文在三个多模态生物特征分数数据集（即NIST BSSR1、BIOCOP2008和MIT LL Trimodal）上评估了多种分数插补方法，并探究了可能影响插补效果的因素。我们的研究得出三项关键结论：（1）即便融合规则不要求完整的分数数据，对缺失分数进行插补也优于不插补。（2）平衡训练数据中的类别对于缓解插补技术对代表性不足类别的负面偏差至关重要，即便这需要丢弃大量分数向量。（3）当模态间的分数存在相关性时，多变量插补方法似乎更具优势，而单变量方法则更适用于模态间分数相关性较低的场景。
英文摘要：
Combining match scores from different biometric systems via fusion is a well-established approach to improving recognition accuracy. However, missing scores can degrade performance as well as limit the possible fusion techniques that can be applied. Imputation is a promising technique in multibiometric systems for replacing missing data. In this paper, we evaluate various score imputation approaches on three multimodal biometric score datasets, viz. NIST BSSR1, BIOCOP2008, and MIT LL Trimodal, and investigate the factors which might influence the effectiveness of imputation. Our studies reveal three key observations: (1) Imputation is preferable over not imputing missing scores, even when the fusion rule does not require complete score data. (2) Balancing the classes in the training data is crucial to mitigate negative biases in the imputation technique towards the under-represented class, even if it involves dropping a substantial number of score vectors. (3) Multivariate imputation approaches seem to be beneficial when scores between modalities are correlated, while univariate approaches seem to benefit scenarios where scores between modalities are less correlated.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
