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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Majumdar, A. Ward, R.K. |
| Copyright Year | 2009 |
| Description | Author affiliation: Department of Electrical and Computer Engineering, University of British Columbia, Canada (Majumdar, A.; Ward, R.K.) |
| Abstract | SIFT (Scale Invariant Feature Transform) features are widely used in object recognition. These features are invariant to changes in scale, 2D translation and rotation transformations. To a limited extent they are also robust to 3D projection transformations. SIFT Features however, are of very high dimension and large number of SIFT features are generated from an image. The large computational effort associated with matching all the SIFT features for recognition tasks, limits its application to face recognition problems. In this work we propose a discriminative ranking of SIFT features that can be used to prune the number of SIFT features for face recognition. Our method checks the number of irrelevant features to be matched thereby reducing the computational complexity. In the process it also increases the recognition accuracy. We show that the reduction in the number of computations is more than 4 times and increase in the recognition accuracy is 1% on average. Experimental results confirm that our proposed recognition method is robust to changes in head pose, illumination, facial expression and partial occlusion. |
| Starting Page | 27 |
| Ending Page | 30 |
| File Size | 184820 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424435098 |
| ISSN | 08407789 |
| DOI | 10.1109/CCECE.2009.5090085 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-05-03 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Face recognition Lighting Robustness Magnetic heads Object recognition Image generation Image databases Spatial databases Face detection Computational complexity computer vision face recognition |
| Content Type | Text |
| Resource Type | Article |
| Subject | Electrical and Electronic Engineering Hardware and Architecture |
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