Comparative study using eigenface and fisherface on face recognition system

Purnawan, Andri (2011) Comparative study using eigenface and fisherface on face recognition system. Other thesis, Universitas Al Azhar Indonesia.

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Abstract

This final project describes a study of two traditional face recognition methods, the Eigenface [2] and the Fisherface method [4]. The Eigenface is the first method considered as a successful technique of face recognition. The Eigenface method uses Principal Component Analysis (PCA) to linearly project the image space to a low dimensional feature space. The Fisherface method is an enhancement of the Eigenface method that it uses Fisher`s Linear Discriminant Analysis (FLDA or LDA) for the dimensionality reduction. The LDA maximizes the ratio of between-class scatter and minimizes the ratio of within-class scatter, therefore, it works better than PCA for purpose of discrimination. The Fisherface is especially useful when facial images have large variations in pose, facial expression, illumination and also size of training database face images. In this final project, a comparison of the Eigenface and the Fisherface methods respect to facial images having large pose, variations of expression and also size of training database face images are examined.

Item Type: Thesis (Other)
Additional Information: Identifier : EL 11 024 Language : Inggris Copyright : Attribution 4.0. International
Subjects: Library of Congress Subject Areas > Skripsi
Library of Congress Subject Areas > Skripsi

Library of Congress Subject Areas > Human face recognition (Computer science)
Library of Congress Subject Areas > Human face recognition (Computer science)
Divisions: University Structure > Universitas Al Azhar Indonesia > Universitas Al Azhar Indonesia - Fakultas Sains dan Teknologi
Depositing User: Rahman Pujianto
Date Deposited: 19 Jul 2018 04:59
Last Modified: 19 Jul 2018 04:59
URI: http://eprints.uai.ac.id/id/eprint/427

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