Personalized machine learning for facial expression analysis
Name
1078783584-MIT.pdf
Description
Full printable version
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747.2 KB
Format
Adobe PDF
Checksum (MD5)
4906ff7af8003e4f78a847133f1c8f80
Author(s)
Feffer, Michael A. (Michael Anthony)
Advisor(s)
Ognjen (Oggi) Rudovic and Rosalind Picard.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
For this MEng Thesis Project, I investigated the personalization of deep convolutional networks for facial expression analysis. While prior work focused on population-based ("one-size-fits-all") models for prediction of affective states (valence/arousal), I constructed personalized versions of these models to improve upon state-of-the-art general models through solving a domain adaptation problem. This was done by starting with pre-trained deep models for face analysis and fine-tuning the last layers to specific subjects or subpopulations. For prediction, a "mixture of experts" (MoE) solution was employed to select the proper outputs based on the given input. The research questions answered in this project are: (1) What are the effects of model personalization on the estimation of valence and arousal from faces? (2) What is the amount of (un)supervised data needed to reach a target performance? Models produced in this research provide the foundation of a novel tool for personalized real-time estimation of target metrics.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 35-36).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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