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Face recognition using eigen-face implemented on dsp professor

Master of Science in Embedded System Design Engineering

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Bibliographic Details
Main Author: Nawaf Hazim, Naf Barnouti
Other Authors: Muhammad Imran, Ahmad, Dr.
Format: Dissertation
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2014
Subjects:
Human face recognition
DSP processor
Face recognition
Automatic face recognition
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/72272
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spelling my.unimap-722722021-09-30T02:19:09Z Face recognition using eigen-face implemented on dsp professor Nawaf Hazim, Naf Barnouti Muhammad Imran, Ahmad, Dr. Human face recognition DSP processor Face recognition Automatic face recognition Master of Science in Embedded System Design Engineering 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. 2014 2021-09-30T02:19:09Z 2021-09-30T02:19:09Z Dissertation http://dspace.unimap.edu.my:80/xmlui/handle/123456789/72272 en Universiti Malaysia Perlis (UniMAP) Universiti Malaysia Perlis (UniMAP) School of Computer and Communication Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Human face recognition
DSP processor
Face recognition
Automatic face recognition
spellingShingle Human face recognition
DSP processor
Face recognition
Automatic face recognition
Nawaf Hazim, Naf Barnouti
Face recognition using eigen-face implemented on dsp professor
description Master of Science in Embedded System Design Engineering
author2 Muhammad Imran, Ahmad, Dr.
author_facet Muhammad Imran, Ahmad, Dr.
Nawaf Hazim, Naf Barnouti
format Dissertation
author Nawaf Hazim, Naf Barnouti
author_sort Nawaf Hazim, Naf Barnouti
title Face recognition using eigen-face implemented on dsp professor
title_short Face recognition using eigen-face implemented on dsp professor
title_full Face recognition using eigen-face implemented on dsp professor
title_fullStr Face recognition using eigen-face implemented on dsp professor
title_full_unstemmed Face recognition using eigen-face implemented on dsp professor
title_sort face recognition using eigen-face implemented on dsp professor
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2014
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/72272
_version_ 1724609872939450368
score 13.214268

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