Active one-shot learning for personalized human affect estimation
Name
1078150914-MIT.pdf
Description
Full printable version
Size
3.3 MB
Format
Adobe PDF
Checksum (MD5)
ccd2f678554c68a16a377ae5dd25e32d
Author(s)
Xu, Jacqueline L
Advisor(s)
Ognjen Rudovic and Rosalind Picard.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Building models that can classify human affect leads to the challenge of learning on data that is complex in features and limited in size and labels. How can these models balance being general and personalized, capturing both the commonalities and the individual quirks of people? While previous research has explored the intersection of deep learning, active learning, and one-shot learning to craft models that are semi-supervised and data-efficient, these methods have not yet been examined in the context of personalized affective computing. This study presents a novel active one-shot learning model for personalized estimation of human affect, in particular, detection of pain from facial expressions. The model demonstrates the ability to learn an active learner that achieves high accuracy, learns to become data efficient, and introduces model personalization to match or outperform fully supervised and population-level models.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 43-46).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Persistent DSpace Link
