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FACE CLASSIFICATION FOR AUTHENTICATION APPROACH BY USING WAVELET TRANSFORM AND STATISTICAL FEATURES SELECTION

This thesis consists of three parts: face localization, features selection and classification process. Three methods were proposed to locate the face region in the input image. Two of them based on pattern (template) Matching Approach, and the other based on clustering approach. Five datasets of fac...

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Main Author: DAWOUD JADALAH, NADIR NOURAIN
Format: Thesis
Language:English
Published: 2011
Online Access:http://utpedia.utp.edu.my/3053/1/Nadir_Norain-final-FINAL_thesis.pdf
http://utpedia.utp.edu.my/3053/
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spelling my-utp-utpedia.30532017-01-25T09:42:44Z http://utpedia.utp.edu.my/3053/ FACE CLASSIFICATION FOR AUTHENTICATION APPROACH BY USING WAVELET TRANSFORM AND STATISTICAL FEATURES SELECTION DAWOUD JADALAH, NADIR NOURAIN This thesis consists of three parts: face localization, features selection and classification process. Three methods were proposed to locate the face region in the input image. Two of them based on pattern (template) Matching Approach, and the other based on clustering approach. Five datasets of faces namely: YALE database, MIT-CBCL database, Indian database, BioID database and Caltech database were used to evaluate the proposed methods. For the first method, the template image is prepared previously by using a set of faces. Later, the input image is enhanced by applying n-means kernel to decrease the image noise. Then Normalized Correlation (NC) is used to measure the correlation coefficients between the template image and the input image regions. For the second method, instead of using n-means kernel, an optimized metrics are used to measure the difference between the template image and the input image regions. In the last method, the Modified K-Means Algorithm was used to remove the non-face regions in the input image. The above-mentioned three methods showed accuracy of localization between 98% and 100% comparing with the existed methods. In the second part of the thesis, Discrete Wavelet Transform (DWT) utilized to transform the input image into number of wavelet coefficients. Then, the coefficients of weak statistical energy less than certain threshold were removed, and resulted in decreasing the primary wavelet coefficients number up to 98% out of the total coefficients. Later, only 40% statistical features were extracted from the hight energy features by using the variance modified metric. During the experimental (ORL) Dataset was used to test the proposed statistical method. Finally, Cluster-K-Nearest Neighbor (C-K-NN) was proposed to classify the input face based on the training faces images. The results showed a significant improvement of 99.39% in the ORL dataset and 100% in the Face94 dataset classification accuracy. Moreover, a new metrics were introduced to quantify the exactness of classification and some errors of the classification can be corrected. All the above experiments were implemented in MATLAB environment. 2011 Thesis NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/3053/1/Nadir_Norain-final-FINAL_thesis.pdf DAWOUD JADALAH, NADIR NOURAIN (2011) FACE CLASSIFICATION FOR AUTHENTICATION APPROACH BY USING WAVELET TRANSFORM AND STATISTICAL FEATURES SELECTION. Masters thesis, UNIVERSITI TEKNOLOGI PETRONAS.
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
description This thesis consists of three parts: face localization, features selection and classification process. Three methods were proposed to locate the face region in the input image. Two of them based on pattern (template) Matching Approach, and the other based on clustering approach. Five datasets of faces namely: YALE database, MIT-CBCL database, Indian database, BioID database and Caltech database were used to evaluate the proposed methods. For the first method, the template image is prepared previously by using a set of faces. Later, the input image is enhanced by applying n-means kernel to decrease the image noise. Then Normalized Correlation (NC) is used to measure the correlation coefficients between the template image and the input image regions. For the second method, instead of using n-means kernel, an optimized metrics are used to measure the difference between the template image and the input image regions. In the last method, the Modified K-Means Algorithm was used to remove the non-face regions in the input image. The above-mentioned three methods showed accuracy of localization between 98% and 100% comparing with the existed methods. In the second part of the thesis, Discrete Wavelet Transform (DWT) utilized to transform the input image into number of wavelet coefficients. Then, the coefficients of weak statistical energy less than certain threshold were removed, and resulted in decreasing the primary wavelet coefficients number up to 98% out of the total coefficients. Later, only 40% statistical features were extracted from the hight energy features by using the variance modified metric. During the experimental (ORL) Dataset was used to test the proposed statistical method. Finally, Cluster-K-Nearest Neighbor (C-K-NN) was proposed to classify the input face based on the training faces images. The results showed a significant improvement of 99.39% in the ORL dataset and 100% in the Face94 dataset classification accuracy. Moreover, a new metrics were introduced to quantify the exactness of classification and some errors of the classification can be corrected. All the above experiments were implemented in MATLAB environment.
format Thesis
author DAWOUD JADALAH, NADIR NOURAIN
spellingShingle DAWOUD JADALAH, NADIR NOURAIN
FACE CLASSIFICATION FOR AUTHENTICATION APPROACH BY USING WAVELET TRANSFORM AND STATISTICAL FEATURES SELECTION
author_facet DAWOUD JADALAH, NADIR NOURAIN
author_sort DAWOUD JADALAH, NADIR NOURAIN
title FACE CLASSIFICATION FOR AUTHENTICATION APPROACH BY USING WAVELET TRANSFORM AND STATISTICAL FEATURES SELECTION
title_short FACE CLASSIFICATION FOR AUTHENTICATION APPROACH BY USING WAVELET TRANSFORM AND STATISTICAL FEATURES SELECTION
title_full FACE CLASSIFICATION FOR AUTHENTICATION APPROACH BY USING WAVELET TRANSFORM AND STATISTICAL FEATURES SELECTION
title_fullStr FACE CLASSIFICATION FOR AUTHENTICATION APPROACH BY USING WAVELET TRANSFORM AND STATISTICAL FEATURES SELECTION
title_full_unstemmed FACE CLASSIFICATION FOR AUTHENTICATION APPROACH BY USING WAVELET TRANSFORM AND STATISTICAL FEATURES SELECTION
title_sort face classification for authentication approach by using wavelet transform and statistical features selection
publishDate 2011
url http://utpedia.utp.edu.my/3053/1/Nadir_Norain-final-FINAL_thesis.pdf
http://utpedia.utp.edu.my/3053/
_version_ 1739830993406132224
score 13.211869

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