Graduation Year
2021
Document Type
Thesis
Degree
M.S.C.S.
Degree Name
MS in Computer Science (M.S.C.S.)
Degree Granting Department
Computer Science and Engineering
Major Professor
Paul Rosen, Ph.D.
Committee Member
Shaun Canavan, Ph.D.
Committee Member
Lawrence Hall, Ph.D.
Keywords
Data Visualization, Dimension Reduction
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 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.
https://digitalcommons.usf.edu/etd/9103
