论文标题
陈述比较得分不确定性和验证决策对透明面部识别的信心
Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition
论文作者
论文摘要
面部识别（FR）越来越多地用于批判性验证决策中，因此，需要评估此类决策的可信度。决策的信心通常基于模型的整体性能或图像质量。我们建议将模型不确定性传播到分数和决策中，以提高验证决策的透明度。这项工作提出了两项贡献。首先，我们提出了一种方法来估计面部比较得分的不确定性。其次，我们介绍了对系统决定提供有关验证决定的洞察力的信心度量。比较得分不确定性和验证决策信心的适用性已在两个数据集上的三个面部识别模型上得到了证明。
Face Recognition (FR) is increasingly used in critical verification decisions and thus, there is a need for assessing the trustworthiness of such decisions. The confidence of a decision is often based on the overall performance of the model or on the image quality. We propose to propagate model uncertainties to scores and decisions in an effort to increase the transparency of verification decisions. This work presents two contributions. First, we propose an approach to estimate the uncertainty of face comparison scores. Second, we introduce a confidence measure of the system's decision to provide insights into the verification decision. The suitability of the comparison scores uncertainties and the verification decision confidences have been experimentally proven on three face recognition models on two datasets.
