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
Shaun Canavan, Ph.D.
Committee Member
Dmitry B. Goldgof, Ph.D.
Committee Member
Srinivas Katkoori, Ph.D.
Keywords
Engagement Prediction, Gaze Detection, Physiological Signals
Abstract
In this work, I will present our approach of using multi-modal data for recognizing human emotion and behavior in the wild. The study is divided into four tasks: group emotion recognition, driver gaze prediction, student engagement prediction, and emotion recognition using physiological signals. We explore multiple approaches including classical machine learning tools such as random forests, state-of-the-art deep neural networks, and multiple fusion and ensemble-based approaches. We also show that similar approaches can be used across tracks as many of the features generalize well to the different problems (e.g. facial features).
Scholar Commons Citation
Srivastava, Shivam, "Recognizing Emotion in the Wild Using Multimodal Data" (2021). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/8873
