Wednesday, September 20, 2017

SRL MS Thesis Defense of Jung In Koh. Thursday, June 15. Title: Developing a Hand Gesture Recognition System for Mapping Symbolic Hand Gestures to Analogous Emoji in Computer-Mediated Communication

Thesis Defense
Thursday, June 15

Title: Developing a Hand Gesture Recognition System for Mapping Symbolic Hand Gestures to Analogous Emoji in Computer-Mediated Communication



Jung In Koh

1 PM Thursday, June 15, 2017

Teague 326

Abstract



Recent trends in computer-mediated communications (CMC) have not only led to expanded instant messaging (IM) 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 come 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 (feature-based, shape based) that distinguishes the user-defined hand gestures. The goal of this research is to provide 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%. 

Biography

Jung In Koh is a Master's student in the Department of Computer Science and Engineering at Texas A&M University and a research assistant in the Sketch Recognition Lab. Before joining Texas A&M, she received the bachelor's degree in Computer Science from Sookmyung Women's University in South Korea. Her research interests include motion-detection and data mining.

Advisor: Dr. Tracy Hammond

 

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