Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/303068
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dc.coverage.spatialDevelopment and comparison of facial expression recognition methods using machine learning techniques
dc.date.accessioned2020-10-16T05:01:10Z-
dc.date.available2020-10-16T05:01:10Z-
dc.identifier.urihttp://hdl.handle.net/10603/303068-
dc.description.abstractCorrespondence in any form either verbal or non verbal is indispensable to finish different every day routine tasks and assumes a significant part throughout everyday life The rapid advancement in intelligent frameworks creates a gigantic hole which is isolating the people individually Ongoing endeavors to overcome any issues incorporate endeavors to perceive motions and nonverbal articulations of the user consequently The face is the reflection of the brain facial expressions are the discernible consequences of moving a facial muscle In the perception of human articulations the facial expression assumes a fundamental part In regular Human Machine Interfaces HMI the recognition of outward appearance has been considered as an essential module Facial Expression FE is an unmistakable show of the intellectual movement emotional position identity and psychopathology of a human It doesnt simply express our behaviors at the same time it additionally offers fundamental talkative signals amid social association For the past forty years the researchers in a computer vision domain have been showing much enthusiasm for breaking down and to naturally perceive outward appearances Automatic Facial Expression Recognition process newlinecomprises of three stages Face detection Feature extraction and Expression classifications To construct vigorous facial expression recognition system that is equipped for creating reliable outcomes it is important to extract features that have hard discriminative features newline newline
dc.format.extentxxii, 155p.
dc.languageEnglish
dc.relationp.148-154.
dc.rightsuniversity
dc.titleDevelopment and comparison of facial expression recognition methods using machine learning techniques
dc.title.alternative
dc.creator.researcherCarmel Sobia M
dc.subject.keywordEngineering and Technology
dc.subject.keywordEngineering
dc.subject.keywordEngineering Electrical and Electronic
dc.subject.keywordFacial expression
dc.subject.keywordPsychopathology
dc.subject.keywordHuman Machine Interfaces
dc.description.note
dc.contributor.guideAbudhahir A
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Electrical Engineering
dc.date.registered
dc.date.completed2019
dc.date.awarded2019
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Electrical Engineering

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02_certificates.pdf1 MBAdobe PDFView/Open
03_abstracts.pdf337.99 kBAdobe PDFView/Open
04_acknowledgements.pdf436.66 kBAdobe PDFView/Open
05_contents.pdf342.55 kBAdobe PDFView/Open
06_list_of_tables.pdf517.87 kBAdobe PDFView/Open
07_list_of_figures.pdf218.58 kBAdobe PDFView/Open
08_list_of_abbreviations.pdf529.74 kBAdobe PDFView/Open
09_chapter1.pdf673.29 kBAdobe PDFView/Open
10_chapter2.pdf482.39 kBAdobe PDFView/Open
11_chapter3.pdf1.79 MBAdobe PDFView/Open
12_chapter4.pdf1.23 MBAdobe PDFView/Open
13_chapter5.pdf1.38 MBAdobe PDFView/Open
14_conclusion.pdf239.04 kBAdobe PDFView/Open
15_references.pdf368.66 kBAdobe PDFView/Open
16_list_of_publications.pdf338.15 kBAdobe PDFView/Open
80_recommendation.pdf213.64 kBAdobe PDFView/Open


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