Presentation | 2000/9/15 Refinements of Virtual Subspace Method for Robust Face Recognition in Natural Lighting Conditions Kazuma Shigenari, Takeshi Shakunaga, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | The virtual subspace method was proposed in [7] for the robust face recognition independent of lighting conditions. This paper refines the original method in order to reduce computational costs and to achieve a high recognition rate. Some refinements are made with the original method by (1) introduction of the normalized correlation as the similarity measure, (2) construction of the virtual subspace in the canonical space, and (3) decomposition of the similarity measure into two orthogonal factors. Final results show that 89.7% correct discrimination is made over 50 persons when only one image is registered for each person. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Virtual subspace method / face recognition / canonical eigenspace / lighting compensation / normalized correlation / projection residual |
Paper # | PRMU2000-79 |
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Committee | PRMU |
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Conference Date | 2000/9/15(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Pattern Recognition and Media Understanding (PRMU) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Refinements of Virtual Subspace Method for Robust Face Recognition in Natural Lighting Conditions |
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Keyword(1) | Virtual subspace method |
Keyword(2) | face recognition |
Keyword(3) | canonical eigenspace |
Keyword(4) | lighting compensation |
Keyword(5) | normalized correlation |
Keyword(6) | projection residual |
1st Author's Name | Kazuma Shigenari |
1st Author's Affiliation | Department of Information Technology, Okayama University() |
2nd Author's Name | Takeshi Shakunaga |
2nd Author's Affiliation | Department of Information Technology, Okayama University |
Date | 2000/9/15 |
Paper # | PRMU2000-79 |
Volume (vol) | vol.100 |
Number (no) | 312 |
Page | pp.pp.- |
#Pages | 8 |
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