AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing
This work addresses the need for more robust and explainable emotion recognition in affective computing, though it appears incremental as it builds on existing methods.
The authors tackled the problem of recognizing human emotions from facial poses by applying Topological Data Analysis to capture structural patterns, confirming that their approach distinguishes between emotions and individuals.
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.