In this paper, we investigate the problem of predictive 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 in such high-risk tasks. To address this issue, we first introduce a novel yet simple confidence measure for face and kinship verification, which allows the verification models to transform the similarity score into a confidence score for a given face pair. We further propose a confidence-calibrated approach called angular scaling calibration (ASC). ASC is easy to implement and can be directly applied to existing face and kinship verification models without model modifications, yielding accuracy-preserving and confidence-calibrated probabilistic verification models. To the best of our knowledge, our approach is the first general confidence-calibrated solution to face and kinship verification in a modern context. We conduct extensive experiments on four widely used face and kinship verification datasets, and the results demonstrate the effectiveness of our approach.
翻译:在本文中,我们研究了对面相和亲属关系核查的预测信任度问题。大多数现有面相和亲属核查方法侧重于准确性表现,而忽视了对其预测结果的信任度估计。然而,对信任度的估算对于模拟此类高风险任务的可靠性至关重要。为了解决这一问题,我们首先为面相和亲属关系核查采用一种新颖而简单的信任度度度度度标准,使核查模型能够将相似性评分转化为对特定面相和亲属关系核查的可信度分数。我们进一步提出一种称为三角比例校准(ASC)的信任度校准方法。 ASC很容易实施,并且可以直接应用于现有的面相和亲属关系核查模型,而无需模型修改,产生准确性保留和以信任度校准的概率核查模型。据我们所知,我们的方法是第一个在现代环境下对面相向和亲属关系核查的总体信任度比值解决方案。我们对四套广泛使用的面相和亲属关系核查数据集进行了广泛的实验,结果表明我们的方法的有效性。