# Facial Expressions as a Vulnerability in Face Recognition

arXiv: https://arxiv.org/abs/2011.08809 · HTML: https://mlsanity.com/paper/2011.08809/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 11 of 11)

- [A Comprehensive Study on Face Recognition Biases Beyond Demographics](https://mlsanity.com/paper/2103.01592/) (2021)
- [MIRROR: Multimodal Cognitive Reframing Therapy for Rolling with Resistance](https://mlsanity.com/paper/2504.13211/) (2025)
- [IFBiD: Inference-Free Bias Detection](https://mlsanity.com/paper/2109.04374/) (2021)
- [Introduction to Presentation Attack Detection in Face Biometrics and Recent Advances](https://mlsanity.com/paper/2111.11794/) (2021)
- [Affective Computing Has Changed: The Foundation Model Disruption](https://mlsanity.com/paper/2409.08907/) (2024)
- [NeutrEx: A 3D Quality Component Measure on Facial Expression Neutrality](https://mlsanity.com/paper/2308.09963/) (2023)
- [Is It Really You? Exploring Biometric Verification Scenarios in Photorealistic Talking-Head Avatar Videos](https://mlsanity.com/paper/2508.00748/) (2025)
- [Comprehensive Equity Index (CEI): Definition and Application to Bias Evaluation in Biometrics](https://mlsanity.com/paper/2409.01928/) (2024)
- [Mitigating Algorithmic Bias on Facial Expression Recognition](https://mlsanity.com/paper/2312.15307/) (2023)
- [Towards End-to-End Neural Face Authentication in the Wild -- Quantifying and Compensating for Directional Lighting Effects](https://mlsanity.com/paper/2104.03854/) (2021)
- [Efficient Expression Neutrality Estimation with Application to Face Recognition Utility Prediction](https://mlsanity.com/paper/2402.05548/) (2024)

## Abstract (arXiv)

This work explores facial expression bias as a security vulnerability of face recognition systems. Despite the great performance achieved by state-of-the-art face recognition systems, the algorithms are still sensitive to a large range of covariates. We present a comprehensive analysis of how facial expression bias impacts the performance of face recognition technologies. Our study analyzes: i) facial expression biases in the most popular face recognition databases; and ii) the impact of facial expression in face recognition performances. Our experimental framework includes two face detectors, three face recognition models, and three different databases. Our results demonstrate a huge facial expression bias in the most widely used databases, as well as a related impact of face expression in the performance of state-of-the-art algorithms. This work opens the door to new research lines focused on mitigating the observed vulnerability.