3-D Face Recognition System using Additional Feature Lines in Nearest Feature Line Method in Eigenspace Representation

The additional feature lines can be acquired by projecting each feature point to other feature lines in the same class without increasing the number of feature points. With these additional lines, the system will have the ability to capture more variations of face images, so it can increase the reco...

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Main Authors: Lina, Benyamin Kusumoputro
Format: Article
Language:English
Published: Universitas Indonesia 2003-04-01
Series:Makara Seri Sains
Subjects:
Online Access:http://journal.ui.ac.id/science/article/view/269/265
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spelling doaj-7f49a723c9ae4347bcf4486c38c4798e2020-11-25T00:50:12ZengUniversitas IndonesiaMakara Seri Sains1693-66712003-04-010711103-D Face Recognition System using Additional Feature Lines in Nearest Feature Line Method in Eigenspace RepresentationLinaBenyamin KusumoputroThe additional feature lines can be acquired by projecting each feature point to other feature lines in the same class without increasing the number of feature points. With these additional lines, the system will have the ability to capture more variations of face images, so it can increase the recognition rate of the system. The authors also propose KL-TSubspace1 and KL-TSubspace2 as methods in transforming the 3-D face images from its spatial domain to their eigenspace domain. The experiments use the 3-D human faces of Indonesian people in various expressions and positions. Then, the system is applied to recognize unknown face images with different viewpoints. Experimental results shown that the system using KL-TSubspace2 and Modified Nearest Feature Line method can have the highest recognition rate of 99.17%.http://journal.ui.ac.id/science/article/view/269/2653-D face recognition systemnearest feature line method (NFL)modified nearest feature line method (MNFL)Karhunen-Loeve transformationeigenspace representation
collection DOAJ
language English
format Article
sources DOAJ
author Lina
Benyamin Kusumoputro
spellingShingle Lina
Benyamin Kusumoputro
3-D Face Recognition System using Additional Feature Lines in Nearest Feature Line Method in Eigenspace Representation
Makara Seri Sains
3-D face recognition system
nearest feature line method (NFL)
modified nearest feature line method (MNFL)
Karhunen-Loeve transformation
eigenspace representation
author_facet Lina
Benyamin Kusumoputro
author_sort Lina
title 3-D Face Recognition System using Additional Feature Lines in Nearest Feature Line Method in Eigenspace Representation
title_short 3-D Face Recognition System using Additional Feature Lines in Nearest Feature Line Method in Eigenspace Representation
title_full 3-D Face Recognition System using Additional Feature Lines in Nearest Feature Line Method in Eigenspace Representation
title_fullStr 3-D Face Recognition System using Additional Feature Lines in Nearest Feature Line Method in Eigenspace Representation
title_full_unstemmed 3-D Face Recognition System using Additional Feature Lines in Nearest Feature Line Method in Eigenspace Representation
title_sort 3-d face recognition system using additional feature lines in nearest feature line method in eigenspace representation
publisher Universitas Indonesia
series Makara Seri Sains
issn 1693-6671
publishDate 2003-04-01
description The additional feature lines can be acquired by projecting each feature point to other feature lines in the same class without increasing the number of feature points. With these additional lines, the system will have the ability to capture more variations of face images, so it can increase the recognition rate of the system. The authors also propose KL-TSubspace1 and KL-TSubspace2 as methods in transforming the 3-D face images from its spatial domain to their eigenspace domain. The experiments use the 3-D human faces of Indonesian people in various expressions and positions. Then, the system is applied to recognize unknown face images with different viewpoints. Experimental results shown that the system using KL-TSubspace2 and Modified Nearest Feature Line method can have the highest recognition rate of 99.17%.
topic 3-D face recognition system
nearest feature line method (NFL)
modified nearest feature line method (MNFL)
Karhunen-Loeve transformation
eigenspace representation
url http://journal.ui.ac.id/science/article/view/269/265
work_keys_str_mv AT lina 3dfacerecognitionsystemusingadditionalfeaturelinesinnearestfeaturelinemethodineigenspacerepresentation
AT benyaminkusumoputro 3dfacerecognitionsystemusingadditionalfeaturelinesinnearestfeaturelinemethodineigenspacerepresentation
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