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

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