Human Gesture and Micro-gesture Analysis: Datasets, Methods, and Applications
Thesis event information
Date and time of the thesis defence
Place of the thesis defence
L5, Linnanmaa Campus
Topic of the dissertation
Human Gesture and Micro-gesture Analysis: Datasets, Methods, and Applications
Doctoral candidate
Master of Science Haoyu Chen
Faculty and unit
University of Oulu Graduate School, Faculty of Information Technology and Electrical Engineering, Center for Machine Vision and Signal Analysis
Subject of study
Computer Science
Opponent
Professor Joni-Kristian Kämäräinen, Tampere University
Custos
Academy professor Guoying Zhao, Center for Machine Vision and Signal Analysis
Use subtle body gestures to read your hidden emotions and generate them
Using machines to achieve robust recognition, generation, and even further interpretation of body gestures (e.g., understanding emotion with micro-gestures) is an appealing topic and drives all the research work in this dissertation.
To facilitate the research on this topic with computer vision methods, this dissertation tries to approach it via four stages: regular gesture recognition, micro-gesture dataset and analysis, gesture generation, and specific applications.
Results show that we can achieve robust gesture cognition, realistic gesture generation, and also emotion recognition with gestures.
To facilitate the research on this topic with computer vision methods, this dissertation tries to approach it via four stages: regular gesture recognition, micro-gesture dataset and analysis, gesture generation, and specific applications.
Results show that we can achieve robust gesture cognition, realistic gesture generation, and also emotion recognition with gestures.
Last updated: 23.1.2024