Date of Award
5-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Electrical Engineering and Computer Science
First Advisor
Michael C. King
Second Advisor
Vanessa Edkins
Third Advisor
Marius Silaghi
Fourth Advisor
Karl Ricanek
Abstract
Personality research seeks to explain the wide range of human behaviors through stable, measurable traits. Human interactions are inherently rich and multidimensional, and analyzing behavioral data offers a promising path to uncover personality insights. The increasing convergence of psychology, computer science, and machine learning has fueled interest in computational approaches to personality assessment. Advances in sensing technologies have made it possible to capture fine-grained information about individuals’ behaviors and interactions in naturalistic and controlled environments. Automated audio-visual analysis techniques extract relevant behavioral cues, which machine learning models then interpret to infer underlying personality traits. This work provides a comprehensive overview of personality models and traces the evolution of personality computing. It surveys key interdisciplinary contributions, discusses widely used datasets, and highlights common methodological challenges. The use of facial action units (FAUs) and head movements are explored for predicting personality traits, alongside the creation of an open-source processing framework with heatmap visualizations. Furthermore, we evaluate several sequential modeling techniques—including LSTMs, 3DCNNs, and Transformers for personality classification from dynamic visual inputs. Building on these insights, we explore a novel application of selective state space models, specifically Video Mamba, which has shown promise in modeling temporal patterns in human behavior. This work contributes to the field of computational psychology by offering a new dataset, reproducible tools, and empirical insights that help advance the understanding of how personality traits can be inferred from real-world behavioral data.
Recommended Citation
Vangara, Kushal, "Personality Trait Recognition through Deep Neural Temporal Modeling of Non-Verbal Behavior" (2025). Theses and Dissertations. 1594.
https://repository.fit.edu/etd/1594