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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/519
DC FieldValueLanguage
dc.contributor.authorHuynh Thanh Quanen_US
dc.date.accessioned2020-03-12T17:03:08Z-
dc.date.available2020-03-12T17:03:08Z-
dc.date.issued2017-
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/519-
dc.description.abstractComputer vision has contributed and stand a big role in research study, industrial business and clinical applications in several years. Human face detection and recognition has been the remarkable features in many vision devices nowadays. Understand the different facial expressions of emotion is essential to gain insights into human cognition and perceptual. Six basic types of emotion typically seen in most cultures on over the world, such as happiness, surprise, sadness, anger, fear, and disgust. The emotion of happiness comprises the most universally general and recognized level of positive emotion. This thesis report will mark the problem to recognize and detect the happiness and unhappiness. The last previous elective report has proposed the solution to detect the human face by using Haar-like feature. Continuing with that result, I extend the face detection problem to facial expression recognition problem by using a machine learning model (neural network). A learning model has the ability to detect the happy face when the boundary of facial expression is unidentified clearly. The implementation is done by using the Matlab software. This thesis also covers the explanation of the mathematical concept as well as the implementation to solve the problem.en_US
dc.language.isoenen_US
dc.publisherVietnamese-German Universityen_US
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectFeature extractionen_US
dc.subjectNeural networken_US
dc.subjectFacial expressionen_US
dc.subjectImage databaseen_US
dc.subjectHappy emotionen_US
dc.titleMachine vision object differentiation and learning approachesen_US
dc.typeThesisen_US
item.grantfulltextrestricted-
item.fulltextWith Fulltext-
item.languageiso639-1other-
Appears in Collections:Electrical Engineering and Information Technology (EEIT)
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