| dc.description.abstract | Recent trends in computer-mediated communications (CMC) have not only led to expanded
instant messaging through the use of images and videos, but have also expanded
traditional text messaging with richer content, so-called visual communication markers
(VCM) such as emoticons, emojis, and stickers. VCMs could prevent a potential loss of
subtle emotional conversation in CMC, which is delivered by nonverbal cues that convey
affective and emotional information. However, as the number of VCMs grows in the selection
set, the problem of VCM entry needs to be addressed. Additionally, conventional
ways for accessing VCMs continues to rely on input entry methods that are not directly
and intimately tied to expressive nonverbal cues. One such form of expressive nonverbal
that does exist and is well-studied comes in the form of hand gestures. In this work,
I propose a user-defined hand gesture set that is highly representative to VCMs and a
two-stage hand gesture recognition system (trajectory-based, shape-based) that
distinguishes the user-defined hand gestures. While the trajectory-based recognizer
distinguishes gestures based on the movements of hands, the shape-based recognizer
classifies gestures based on the shapes of hands. The goal of this research is to allow
users to be more immersed, natural, and quick in generating VCMs through gestures.
The idea is for users to maintain the lower-bandwidth online communication of text
messaging to largely retain its convenient and discreet properties, while also
incorporating the advantages of higher-bandwidth online communication of video
messaging by having users naturally gesture their emotions that are then closely mapped
to VCMs. Results show that the accuracy of user-dependent is approximately 86% and
the accuracy of user-independent is about 82%. | en |
