Accurate face recognition systems are increasingly important in sensitive applications like border control or migration management. Therefore, it becomes crucial to quantify the quality of facial images to ensure that low-quality images are not affecting recognition accuracy. In this context, the current draft of ISO/IEC 29794-5 introduces the concept of component quality to estimate how single factors of variation affect recognition outcomes. In this study, we propose a quality measure (NeutrEx) based on the accumulated distances of a 3D face reconstruction to a neutral expression anchor. Our evaluations demonstrate the superiority of our proposed method compared to baseline approaches obtained by training Support Vector Machines on face embeddings extracted from a pre-trained Convolutional Neural Network for facial expression classification. Furthermore, we highlight the explainable nature of our NeutrEx measures by computing per-vertex distances to unveil the most impactful face regions and allow operators to give actionable feedback to subjects.

翻译：摘要：准确的人脸识别系统在边境管控或移民管理等敏感应用中日显重要。因此，量化面部图像的质量以确保低质量图像不影响识别精度变得至关重要。在此背景下，当前ISO/IEC 29794-5标准草案引入了分量质量（component quality）概念，用于评估单个变化因子对识别结果的影响。本研究中，我们提出了一种基于三维人脸重建与中性表情锚点之间累积距离的质量度量（NeutrEx）。实验评估表明，与通过预训练卷积神经网络提取的人脸嵌入训练支持向量机所获得的基线方法相比，我们提出的方法性能更优。此外，我们通过计算逐顶点距离来揭示最具影响的面部区域，使操作人员能够向受试者提供可操作反馈，从而突显了NeutrEx度量的可解释性。