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http://hdl.handle.net/10603/303068
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DC Field | Value | Language |
---|---|---|
dc.coverage.spatial | Development and comparison of facial expression recognition methods using machine learning techniques | |
dc.date.accessioned | 2020-10-16T05:01:10Z | - |
dc.date.available | 2020-10-16T05:01:10Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/303068 | - |
dc.description.abstract | Correspondence 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.extent | xxii, 155p. | |
dc.language | English | |
dc.relation | p.148-154. | |
dc.rights | university | |
dc.title | Development and comparison of facial expression recognition methods using machine learning techniques | |
dc.title.alternative | ||
dc.creator.researcher | Carmel Sobia M | |
dc.subject.keyword | Engineering and Technology | |
dc.subject.keyword | Engineering | |
dc.subject.keyword | Engineering Electrical and Electronic | |
dc.subject.keyword | Facial expression | |
dc.subject.keyword | Psychopathology | |
dc.subject.keyword | Human Machine Interfaces | |
dc.description.note | ||
dc.contributor.guide | Abudhahir A | |
dc.publisher.place | Chennai | |
dc.publisher.university | Anna University | |
dc.publisher.institution | Faculty of Electrical Engineering | |
dc.date.registered | ||
dc.date.completed | 2019 | |
dc.date.awarded | 2019 | |
dc.format.dimensions | 21cm | |
dc.format.accompanyingmaterial | None | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Faculty of Electrical Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 22.69 kB | Adobe PDF | View/Open |
02_certificates.pdf | 1 MB | Adobe PDF | View/Open | |
03_abstracts.pdf | 337.99 kB | Adobe PDF | View/Open | |
04_acknowledgements.pdf | 436.66 kB | Adobe PDF | View/Open | |
05_contents.pdf | 342.55 kB | Adobe PDF | View/Open | |
06_list_of_tables.pdf | 517.87 kB | Adobe PDF | View/Open | |
07_list_of_figures.pdf | 218.58 kB | Adobe PDF | View/Open | |
08_list_of_abbreviations.pdf | 529.74 kB | Adobe PDF | View/Open | |
09_chapter1.pdf | 673.29 kB | Adobe PDF | View/Open | |
10_chapter2.pdf | 482.39 kB | Adobe PDF | View/Open | |
11_chapter3.pdf | 1.79 MB | Adobe PDF | View/Open | |
12_chapter4.pdf | 1.23 MB | Adobe PDF | View/Open | |
13_chapter5.pdf | 1.38 MB | Adobe PDF | View/Open | |
14_conclusion.pdf | 239.04 kB | Adobe PDF | View/Open | |
15_references.pdf | 368.66 kB | Adobe PDF | View/Open | |
16_list_of_publications.pdf | 338.15 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 213.64 kB | Adobe PDF | View/Open |
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