# AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing

arXiv: https://arxiv.org/abs/2107.08573 · HTML: https://mlsanity.com/paper/2107.08573/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2021.

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

- [MOUNTAINEER: Topology-Driven Visual Analytics for Comparing Local Explanations](https://mlsanity.com/paper/2406.15613/) (2024)
- [Topological Perspectives on Optimal Multimodal Embedding Spaces](https://mlsanity.com/paper/2405.18867/) (2024)
- [Topological Representations of Local Explanations](https://mlsanity.com/paper/2201.02155/) (2022)

## Abstract (arXiv)

We present an approach utilizing Topological Data Analysis to study the structure of face poses used in affective computing, i.e., the process of recognizing human emotion. The approach uses a conditional comparison of different emotions, both respective and irrespective of time, with multiple topological distance metrics, dimension reduction techniques, and face subsections (e.g., eyes, nose, mouth, etc.). The results confirm that our topology-based approach captures known patterns, distinctions between emotions, and distinctions between individuals, which is an important step towards more robust and explainable emotion recognition by machines.