{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/119763"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/119763","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Personalized machine learning for facial expression analysis","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Feffer, Michael A. (Michael Anthony)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Ognjen (Oggi) Rudovic and Rosalind Picard."],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018","date_published":"2018","updated_at":"2026-07-22T22:20:48Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/119763","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Ognjen (Oggi) Rudovic and Rosalind Picard."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Feffer, Michael A. (Michael Anthony)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-12-18T19:49:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-12-18T19:49:00Z"]},{"key":"dc:date.issued","label":"Date","values":["2018"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering and Computer Science."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["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."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/119763"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Personalized machine learning for facial expression analysis"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ognjen (Oggi) Rudovic and Rosalind Picard."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Feffer, Michael A. (Michael Anthony)"],"dc:date.accessioned":["2018-12-18T19:49:00Z"],"dc:date.available":["2018-12-18T19:49:00Z"],"dc:date.issued":["2018"],"dc: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)."],"dc:description.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."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/119763"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Personalized machine learning for facial expression analysis"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:20:48Z"}