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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Universitas Indonesia
2003-04-01
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Online Access: | http://journal.ui.ac.id/science/article/view/269/265 |
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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 |
_version_ |
1725248717769932800 |