NeutrEx: A 3D Quality Component Measure on Facial Expression Neutrality

TL;DR

NeutrEx is a novel 3D facial expression neutrality measure that improves face recognition quality assessment by analyzing face reconstruction distances, providing explainability and actionable feedback.

Contribution

The paper introduces NeutrEx, a new 3D-based quality measure for facial expression neutrality that outperforms baseline methods and offers explainable insights.

Findings

NeutrEx outperforms baseline SVM-based approaches.

It provides explainable, region-specific feedback.

It effectively predicts face recognition quality.

Abstract

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.