Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/303068
Title: Development and comparison of facial expression recognition methods using machine learning techniques
Researcher: Carmel Sobia M
Guide(s): Abudhahir A
Keywords: Engineering and Technology
Engineering
Engineering Electrical and Electronic
Facial expression
Psychopathology
Human Machine Interfaces
University: Anna University
Completed Date: 2019
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
Pagination: xxii, 155p.
URI: http://hdl.handle.net/10603/303068
Appears in Departments:Faculty of Electrical Engineering

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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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