NeutrEx: A 3D Quality Component Measure on Facial Expression Neutrality
This work addresses the need for reliable facial image quality assessment in sensitive applications like border control, though it appears incremental as it builds on existing ISO standards and baseline approaches.
The study tackled the problem of quantifying facial image quality for face recognition by proposing NeutrEx, a measure based on 3D face reconstruction distances to a neutral expression anchor, which demonstrated superiority over baseline methods in evaluations.
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.