论文标题
置信度的面部和亲属验证
Confidence-Calibrated Face and Kinship Verification
论文作者
论文摘要
在本文中，我们研究了对面部和亲属验证的预测信心的问题。大多数现有的面部和亲属验证方法都集中在准确性性能上，同时忽略了其预测结果的置信度估计。但是，置信度估计对于在此类高风险任务中对可靠性和可信赖性进行建模至关重要。为了解决这个问题，我们引入了一种有效的置信度措施，允许验证模型将相似性得分转换为任何给定的面对对的置信得分。我们进一步提出了一种置信度的方法，称为角度缩放校准（ASC）。 ASC易于实现，并且可以轻松地将其应用于现有的验证模型，而无需修改模型，从而产生了准确的概率验证模型。此外，我们介绍了校准信心的不确定性，以在存在嘈杂数据的情况下提高验证模型的可靠性和可信赖性。据我们所知，我们的工作为现代面部和亲属验证任务提供了第一个全面的信心校准解决方案。我们对四个广泛使用的面部和亲属验证数据集进行了广泛的实验，结果证明了我们提出的方法的有效性。代码和型号可在https://github.com/cnulab/asc上找到。
In this paper, we investigate the problem of prediction confidence in face and kinship verification. Most existing face and kinship verification methods focus on accuracy performance while ignoring confidence estimation for their prediction results. However, confidence estimation is essential for modeling reliability and trustworthiness in such high-risk tasks. To address this, we introduce an effective confidence measure that allows verification models to convert a similarity score into a confidence score for any given face pair. We further propose a confidence-calibrated approach, termed Angular Scaling Calibration (ASC). ASC is easy to implement and can be readily applied to existing verification models without model modifications, yielding accuracy-preserving and confidence-calibrated probabilistic verification models. In addition, we introduce the uncertainty in the calibrated confidence to boost the reliability and trustworthiness of the verification models in the presence of noisy data. To the best of our knowledge, our work presents the first comprehensive confidence-calibrated solution for modern face and kinship verification tasks. We conduct extensive experiments on four widely used face and kinship verification datasets, and the results demonstrate the effectiveness of our proposed approach. Code and models are available at https://github.com/cnulab/ASC.
