Active one-shot learning for personalized human affect estimation

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Xu, Jacqueline L

Advisor dc:contributor.advisor

• Ognjen Rudovic and Rosalind Picard.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/119771

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119771