  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.
