{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/92967"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/92967","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Identifying facial landmarks, action units and emotions using deep networks","abstract":"The goal of this thesis it to use deep neural networks, specifically Convolutional Neural Networks (CNNs) to predict facial landmarks, facial action units and emotions and to study the results of intermediate experiments while doing so. Learning the different features of facial images has always been a difficult task and primarily involves using hand-crafted features which would almost definitely ignore some information related to the different dynamics of facial features. We train our network model using the raw facial images and study its effectiveness in predicting facial landmarks, action units and emotions. In this thesis we learnt that CNNs are highly effective in predicting facial landmarks and AUs, mainly because of their ability to learn features from raw images. We also established that feature sets which can effectively outline the different properties of a face are more useful in classifying facial emotions than either images or facial landmarks.","abstract_html":"The goal of this thesis it to use deep neural networks, specifically Convolutional Neural Networks (CNNs) to predict facial landmarks, facial action units and emotions and to study the results of intermediate experiments while doing so. Learning the different features of facial images has always been a difficult task and primarily involves using hand-crafted features which would almost definitely ignore some information related to the different dynamics of facial features. We train our network model using the raw facial images and study its effectiveness in predicting facial landmarks, action units and emotions. In this thesis we learnt that CNNs are highly effective in predicting facial landmarks and AUs, mainly because of their ability to learn features from raw images. We also established that feature sets which can effectively outline the different properties of a face are more useful in classifying facial emotions than either images or facial landmarks.","abstract_has_math":false,"creators":["Prabhu, Namrata"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Hoiem, Derek"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-11-10T18:35:29Z","date_published":"2016-11-10T18:35:29Z","updated_at":"2026-07-22T22:26:35Z","subjects":["Convolutional Neural Networks (CNN)","Facial Landmarks","Expressions","Action Units"],"languages":["en"],"rights":["Copyright 2016 Namrata Prabhu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/92967","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hoiem, Derek"]},{"key":"dc:creator","label":"Author","values":["Prabhu, Namrata"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-11-10T18:35:29Z","2018-11-11T10:15:24Z","2016-07-20","2016-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Convolutional Neural Networks (CNN)","Facial Landmarks","Expressions","Action Units"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Namrata Prabhu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/92967"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The goal of this thesis it to use deep neural networks, specifically Convolutional Neural Networks (CNNs) to predict facial landmarks, facial action units and emotions and to study the results of intermediate experiments while doing so. 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Learning the different features of facial images has always been a difficult task and primarily involves using hand-crafted features which would almost definitely ignore some information related to the different dynamics of facial features. We train our network model using the raw facial images and study its effectiveness in predicting facial landmarks, action units and emotions. In this thesis we learnt that CNNs are highly effective in predicting facial landmarks and AUs, mainly because of their ability to learn features from raw images. We also established that feature sets which can effectively outline the different properties of a face are more useful in classifying facial emotions than either images or facial landmarks.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-08-01","The student, Namrata Prabhu, accepted the attached license on 2016-07-19 at 23:05.","The student, Namrata Prabhu, submitted this Thesis for approval on 2016-07-19 at 23:10.","This Thesis was approved for publication on 2016-07-20 at 09:47.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10005 on 2016-11-10 at 12:27:17","Made available in DSpace on 2016-11-10T18:35:29Z (GMT). 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