# The Power of Properties: Uncovering the Influential Factors in Emotion Classification

arXiv: https://arxiv.org/abs/2404.07867 · HTML: https://mlsanity.com/paper/2404.07867/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Change is Hard: A Closer Look at Subpopulation Shift](https://mlsanity.com/paper/2302.12254/) (2023)
- [Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery](https://mlsanity.com/paper/1702.03877/) (2017)
- [Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information](https://mlsanity.com/paper/1709.01447/) (2017)
- [Understanding and Mitigating Annotation Bias in Facial Expression Recognition](https://mlsanity.com/paper/2108.08504/) (2021)
- [Let's Get the FACS Straight -- Reconstructing Obstructed Facial Features](https://mlsanity.com/paper/2311.05221/) (2023)

## Built on by (most cited first, 3 of 3)

- [Facing Asymmetry -- Uncovering the Causal Link between Facial Symmetry and Expression Classifiers using Synthetic Interventions](https://mlsanity.com/paper/2409.15927/) (2024)
- [Locally Explaining Prediction Behavior via Gradual Interventions and Measuring Property Gradients](https://mlsanity.com/paper/2503.05424/) (2025)
- [Electromyography-Informed Facial Expression Reconstruction for Physiological-Based Synthesis and Analysis](https://mlsanity.com/paper/2503.09556/) (2025)

## Abstract (arXiv)

Facial expression-based human emotion recognition is a critical research area in psychology and medicine. State-of-the-art classification performance is only reached by end-to-end trained neural networks. Nevertheless, such black-box models lack transparency in their decision-making processes, prompting efforts to ascertain the rules that underlie classifiers' decisions. Analyzing single inputs alone fails to expose systematic learned biases. These biases can be characterized as facial properties summarizing abstract information like age or medical conditions. Therefore, understanding a model's prediction behavior requires an analysis rooted in causality along such selected properties. We demonstrate that up to 91.25% of classifier output behavior changes are statistically significant concerning basic properties. Among those are age, gender, and facial symmetry. Furthermore, the medical usage of surface electromyography significantly influences emotion prediction. We introduce a workflow to evaluate explicit properties and their impact. These insights might help medical professionals select and apply classifiers regarding their specialized data and properties.