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| A Novel Mathematical Based Method for Generating Virtual Samples from a Frontal 2D Face Image for Single Training Sample Face Recognition
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Full
text: |
 PDF(431.3KB) |
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Source |
International Journal of Computer Science and Security (IJCSS) |
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Table of Contents |
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Complete Issue PDF(3.68MB) |
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Volume: 5 Issue: 1 |
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Pages: 1-167 |
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Publication
Date: March / April 2011 |
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ISSN
(Online): 1985-1553 |
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Pages |
64 - 71 |
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Author(s) |
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Published
Date |
04-04-2011 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: Face Recognition, Nearest Neighbor, 3D Face Model, 3D Shape, Virtual Images |
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| This Manuscript is indexed in the following databases/websites:- |
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| 1. Directory of Open Access Journals (DOAJ) |
| 2. Scribd |
| 3. iSEEK |
| 4. Docstoc |
| 5. Google Scholar |
| 6. Academic Journals Database |
| 7. Libsearch |
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| This paper deals with one sample face recognition which is a new challenging problem in pattern recognition. In the proposed method, the frontal 2D face image of each person divided to some sub-regions. After computing the 3D shape of each sub-region, a fusion scheme is applied on sub-regions to create a total 3D shape for whole face image. Then, 2D face image is added to the corresponding 3D shape to construct 3D face image. Finally by rotating the 3D face image, virtual samples with different views are generated. Experimental results on ORL dataset using nearest neighbor as classifier reveal an improvement about 5% in recognition rate for one sample per person by enlarging training set using generated virtual samples. Compared with other related works, the proposed method has the following advantages: 1) only one single frontal face is required for face recognition and the outputs are virtual images with variant views for each individual 2) need only 3 key points of face (eyes and nose) 3) 3D shape estimation for generating virtual samples is fully automatic and faster than other 3D reconstruction approaches 4) it is fully mathematical with no training phase and the estimated 3D model is unique for each individual. |
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| Reza Ebrahimpour : Colleagues
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| Masoom Nazari : Colleagues
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| Mehdi Azizi : Colleagues
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| Mahdieh Rezvan : Colleagues
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