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.

翻译：本文研究了人脸与亲属关系验证中的预测置信度问题。现有的大多数人脸与亲属关系验证方法仅关注准确率性能，而忽略了对预测结果的置信度估计。然而，在此类高风险任务中，置信度估计对于建模可靠性与可信度至关重要。为解决这一问题，我们引入了一种有效的置信度度量方法，使验证模型能够将任意给定人脸对的相似度分数转换为置信度分数。我们进一步提出了一种置信度校准方法，即角度缩放校准（ASC）。ASC易于实现，可直接应用于现有验证模型而无需修改模型结构，从而得到保持准确率且置信度校准的概率验证模型。此外，我们在校准置信度中引入不确定性，以增强验证模型在含噪声数据场景下的可靠性与可信度。据我们所知，本研究首次为现代人脸与亲属关系验证任务提供了全面的置信度校准解决方案。我们在四个广泛使用的人脸与亲属关系验证数据集上进行了大量实验，结果证明了所提方法的有效性。代码与模型可在 https://github.com/cnulab/ASC 获取。