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Comparison of Face Recognition Methods

BROWSE_DETAIL_CREATION_DATE: 28-02-2017

BROWSE_DETAIL_IDENTIFIER_SECTION

BROWSE_DETAIL_TYPE: Thesis

BROWSE_DETAIL_SUB_TYPE: Masters

BROWSE_DETAIL_PUBLISH_STATE: Unpublished

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_SUBJECTS: TECHNOLOGY,

BROWSE_DETAIL_CREATORS: Alaisawi , Salem Khalifa Mohamed (Author),

BROWSE_DETAIL_CONTRIBUTERS: Şengül, Gökhan (Advisor),

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Face Recognition, Principal Component Analysis, Speeded Up RobustFeatures, Grey Level Co-Occurrence Matrix


BROWSE_DETAIL_TAB_ABSTRACT

Many studies and researches were conducted in the field of face recognition in orderto get the best accuracy to attain and provide superior results. However, these studiesachieved disparate results in terms of performance and accuracy, thus making itnecessary to conduct studies that compare face recognition algorithms and emergewith results that demonstrate which of these algorithms give the best results.This study aims to compare three face recognition method, namely PrincipleComponent Analysis (PCA), Speeded Up Robust Features (SURF), and Gray-LevelCo-occurrence Matrix (GLCM). This comparison was tested on four imagesdatabases ORL, YALE, FEI, and FERET. The experimental results of this studyshowed that PCA outperformed the other two methods SURF and GLCM whentested on ORL, YALE, FEI, and FERET databases. The results of GLCM were lessaccurate and showed low performance as compared to the rest.


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