Abstract:
Analyzing personality of a subject is an important
aspect of automatic human behavior understanding. Generally,
estimation of the OCEAN (Openness, Conscientiousness,
Extroversion, Agreeableness, Neuroticism) traits are used to
represent the personality of an individual. Personality assessment
based only on individual actions is not sufficient and
considering social context is also important. The focus of this
Ph.D. work is to explore automatic personality assessment
(APA) in the real-world environment. Most of the work till
now in this area has been focusing on BF and related traits
prediction of the subject in lab-controlled environments. There
are several challenges involved in moving from lab-controlled
environments to real-world APA scenarios such as face tracking,
illumination, occlusion and social context. The early work
in this Ph.D. project explores the evolution of graphs, which
capture the interaction patterns and structural changes of a
group of people. We call them Personality Interaction Graphs
(PIG). PIGs are constructed based on the nonverbal cues to
study the behavior of a subject both at a group level and
an individual level. This work brings in the power of PIGs
to improve the prediction accuracy and visualization of the
summary of personality traits with valid cause-effect analysis
at both the individual and group level. Furthermore, various
machine learning techniques to analyze the personality and
emotion of subjects will be explored.
