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

arXiv: https://arxiv.org/abs/2308.09963 · HTML: https://mlsanity.com/paper/2308.09963/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 19 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning an Animatable Detailed 3D Face Model from In-The-Wild Images](https://mlsanity.com/paper/2012.04012/) (2020)
- [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021)
- [3D Morphable Face Models -- Past, Present and Future](https://mlsanity.com/paper/1909.01815/) (2019)
- [EMOCA: Emotion Driven Monocular Face Capture and Animation](https://mlsanity.com/paper/2204.11312/) (2022)
- [Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition](https://mlsanity.com/paper/2104.00232/) (2021)
- [Face Image Quality Assessment: A Literature Survey](https://mlsanity.com/paper/2009.01103/) (2020)
- [Facial Expressions as a Vulnerability in Face Recognition](https://mlsanity.com/paper/2011.08809/) (2020)
- [FEAFA+: An Extended Well-Annotated Dataset for Facial Expression Analysis and 3D Facial Animation](https://mlsanity.com/paper/2111.02751/) (2021)
- [State of the Art of Quality Assessment of Facial Images](https://mlsanity.com/paper/2211.08030/) (2022)

## Built on by (most cited first, 4 of 4)

- [FaceOracle: Chat with a Face Image Oracle](https://mlsanity.com/paper/2501.07202/) (2025)
- [Efficient Expression Neutrality Estimation with Application to Face Recognition Utility Prediction](https://mlsanity.com/paper/2402.05548/) (2024)
- [Eye Sclera for Fair Face Image Quality Assessment](https://mlsanity.com/paper/2501.07158/) (2025)
- [Radial Distortion in Face Images: Detection and Impact](https://mlsanity.com/paper/2501.07179/) (2025)

## Abstract (arXiv)

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.