MS in Computer Science (M.S.C.S.)
Degree Granting Department
Computer Science and Engineering
Paul Rosen, Ph.D.
Shaun Canavan, Ph.D.
Lawrence Hall, Ph.D.
Data Visualization, Dimension Reduction
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 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.
Scholar Commons Citation
Elhamdadi, Hamza, "AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing" (2021). USF Tampa Graduate Theses and Dissertations.