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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/506
DC FieldValueLanguage
dc.contributor.authorNguyen Gia Khanhen_US
dc.date.accessioned2020-03-12T17:02:52Z-
dc.date.available2020-03-12T17:02:52Z-
dc.date.issued2018-
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/506-
dc.description.abstractThis thesis presents two popular techniques for detecting and recognizing of human faces which is totally able to setup and implement in real-time face recognition system. They could be used to identify a face of an object, determine if that is a human face or not and recognize the person by comparing main characteristics of his/her face to images of known people in the database. The main idea shared by these algorithms is that a face would be processed as a two-dimensional mathematical matrix before passing through some calculations to find its main features. All faces images are then projected on the feature space (“face space”) to find the corresponding coordinators. The face space is composed of “Eigenfaces” or “Fisherfaces” which are actually eigenvectors found after doing a matrix composition - Eigendecomposition. At the heart of Eigenface method is the Principal Component Analysis (PCA) - one of the most popular unsupervised learning algorithms - while Fisherface is a better version of the previous one which makes use of both Principal Component Analysis and Linear Discrimination Analysis (LDA) to get more reliable results. Both methods would be examined deeply in their working principles as well as their potential applications in reality before coming to conclusion about the advantage and drawback of each one. The algorithms were realized by Python 3.7 with a Graphical User Interface (GUI). Given initial images in the database, the program can detect and recognize the human faces in the provided pictures before saving them in the database to improve the calculation accuracy in the future. After evaluation, the recognition general results are exported on the screen with details included in the text files. This thesis consists of four main parts: 1. Introduction to Face Recognition. 2. Eigenface Method. 3. Fisherface Method. 4. Implementation and Evaluation.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.subjectFace recognitionen_US
dc.subjectEigenface methoden_US
dc.subjectPrincipal component analysis (PCA)en_US
dc.titleAdvanced methods for face recognitionen_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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