Face recognition using Eigen-Face implemented on DSP processor

Face recognition is the established research area in 2D biometric recognition system and broadly used in a security system. Face recognition system is a physiological biometric information processing based on the two dimensional face image. This thesis focus to develop an automatic face recognition...

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Main Author:	Nawaf Hazim, Naef Barnouti
Format:	Thesis
Language:	English
Subjects:	Eigen-Face Face recognition Face recognition system Automatic face recognition Holistic features extracted
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59817/1/p.1-24..pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59817/2/Full%20Text.pdf
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id	my-unimap-59817
record_format	uketd_dc
spelling	my-unimap-598172019-05-10T04:00:18Z Face recognition using Eigen-Face implemented on DSP processor Nawaf Hazim, Naef Barnouti Dr. Muhammad Imran Ahmad Face recognition is the established research area in 2D biometric recognition system and broadly used in a security system. Face recognition system is a physiological biometric information processing based on the two dimensional face image. This thesis focus to develop an automatic face recognition using holistic features extracted that use the global features represented by low frequency data from face image. Holistic features are extracted using eigenface method where a linear projection technique such as PCA is used to capture the important information in the image. Face image has low frequency information such as shape of mouth, eye, and nose which has high discrimination power. By using PCA, only several number of eigenvector is preserved which belong to these features. A low dimensional feature space is classified using distance classifier. Distance classifier is used to calculate the similarity between two data points in the feature space based on the distance of two vectors. Euclidean distance is used for matching process. The propose method is tested using a benchmark ORL dataset that has 400 images of 40 persons. The best recognition rate is 97.5% when tested using 9 training images and 1 testing image represented with 35 PCA coefficients. Using less number of PCA coefficients is able for the classifier module to be implemented using hardware such as DSP processor. Euclidean distance classifier is tested using the TMS320C6713 digital signal processor (DSP). The computational time is less compared with the offline simulation using PC based. Universiti Malaysia Perlis (UniMAP) 2014 Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/59817 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59817/1/p.1-24..pdf 787e3de5f13262ed166fb2eee539ee77 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59817/2/Full%20Text.pdf 38e395408c4694f8ff000b070edccfd7 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59817/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Eigen-Face Face recognition Face recognition system Automatic face recognition Holistic features extracted School of Computer and Communication Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Dr. Muhammad Imran Ahmad
topic	Eigen-Face Face recognition Face recognition system Automatic face recognition Holistic features extracted
spellingShingle	Eigen-Face Face recognition Face recognition system Automatic face recognition Holistic features extracted Nawaf Hazim, Naef Barnouti Face recognition using Eigen-Face implemented on DSP processor
description	Face recognition is the established research area in 2D biometric recognition system and broadly used in a security system. Face recognition system is a physiological biometric information processing based on the two dimensional face image. This thesis focus to develop an automatic face recognition using holistic features extracted that use the global features represented by low frequency data from face image. Holistic features are extracted using eigenface method where a linear projection technique such as PCA is used to capture the important information in the image. Face image has low frequency information such as shape of mouth, eye, and nose which has high discrimination power. By using PCA, only several number of eigenvector is preserved which belong to these features. A low dimensional feature space is classified using distance classifier. Distance classifier is used to calculate the similarity between two data points in the feature space based on the distance of two vectors. Euclidean distance is used for matching process. The propose method is tested using a benchmark ORL dataset that has 400 images of 40 persons. The best recognition rate is 97.5% when tested using 9 training images and 1 testing image represented with 35 PCA coefficients. Using less number of PCA coefficients is able for the classifier module to be implemented using hardware such as DSP processor. Euclidean distance classifier is tested using the TMS320C6713 digital signal processor (DSP). The computational time is less compared with the offline simulation using PC based.
format	Thesis
author	Nawaf Hazim, Naef Barnouti
author_facet	Nawaf Hazim, Naef Barnouti
author_sort	Nawaf Hazim, Naef Barnouti
title	Face recognition using Eigen-Face implemented on DSP processor
title_short	Face recognition using Eigen-Face implemented on DSP processor
title_full	Face recognition using Eigen-Face implemented on DSP processor
title_fullStr	Face recognition using Eigen-Face implemented on DSP processor
title_full_unstemmed	Face recognition using Eigen-Face implemented on DSP processor
title_sort	face recognition using eigen-face implemented on dsp processor
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Computer and Communication Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59817/1/p.1-24..pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59817/2/Full%20Text.pdf
_version_	1747836839163592704

Face recognition using Eigen-Face implemented on DSP processor

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