There exist many methods for facial recognition, such as
eigenface, templates, artificial neural networks, etc., based on the given facial sample data (patterns). When an input facial image (target) involve simple geometrical transformations and illumination, the performance of these methods are not very satisfactory. In this thesis, following Li et al., we propose a new face recognition system, which can eliminate translation, rotation, scaling, and prospective transformations of facial images automatically, and can also eliminate illumination. According to facial features, we use this method to find the best transformation and the closet illumination, and then to eliminate them for identification by the best matching between a target and the patterns. Finally, we
use the least squares method to recognize the target. This method is validated by numerical examples.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0703106-165315 |
| Date | 03 July 2006 |
| Creators | Chou, Wei-li |
| Contributors | Zi-Cai Li, Yung-Nien Sun, Chung-nan Lee, Y. W. Chiang, Song Wang |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0703106-165315 |
| Rights | unrestricted, Copyright information available at source archive |
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