Human behavior estimation using information that less lead to personal identification
This project aim to build a human behavior estimation using information that less lead to personal identification. In particular, this project proposes a human behavior estimation that does not use information that leads to personal identification (e.g., images including users' faces and clothes, users' voices, or conversations). Instead, this project uses input that includes less personal information such as low-level signals without semantic information.
|Department：||Department of Applied Chemistry|
|Major：||School of Science for Open and Environmental Systems|
|Title：||Senior Assistant Professor|
Research field keywords：Computer Vision, Pattern Recognition, Sensing, Machine Learning
Mariko Isogawa received the B.S., M.S., and Ph.D. degrees from Osaka University, Japan, in 2011, 2013, and 2019, respectively. Since 2013, she has been with Nippon Telegraph and Telephone Corporation as a Researcher. She was a Visiting Scholar with Carnegie Mellon University, USA, from 2019 to 2020. She is currently an Assistant Professor with the Department of Information and Computer Science, Faculty of Science and Technology, Keio University, since 2022. Her research interests include computer vision, pattern recognition, sensing, and machine learning.