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

AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing
Hamza Elhamdadi, Shaun Canavan, and Paul Rosen
IEEE Transactions on Visualization and Computer Graphics (IEEE VIS), 2022

Abstract

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.

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Citation

Hamza Elhamdadi, Shaun Canavan, and Paul Rosen. AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing. IEEE Transactions on Visualization and Computer Graphics (IEEE VIS), 2022.

Bibtex


@article{elhamdadi2021affective,
  title = {{AffectiveTDA}: Using Topological Data Analysis to Improve Analysis and
    Explainability in Affective Computing},
  author = {Elhamdadi, Hamza and Canavan, Shaun and Rosen, Paul},
  journal = {IEEE Transactions on Visualization and Computer Graphics (IEEE VIS)},
  year = {2022},
  note = {textit{Presented at IEEE VIS 2021.}},
  abstract = {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.}
}