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.
翻译：在关键的核查决定中越来越多地使用面部识别(FR),因此,有必要评估这类决定的可信度,决定的可信度往往基于模型的总体性能或图像质量。我们提议将模型的不确定性传播到分数和决定上,以提高核查决定的透明度。这项工作有两种贡献。首先,我们提出一种方法来估计面部比较分数的不确定性。第二,我们提出一种对系统决定的信任度,以便对核查决定提供洞察力。比较分数的不确定性和核查决定的可信度是否合适,已经在两个数据集的三个面对面识别模型上进行了实验性证明。
相关内容
微信扫码咨询专知VIP会员