Face recognition can be used in various biometric applications ranging from identifying criminals entering an airport to identifying an unconscious patient in the hospital With the introduction of 3-dimensional scanners in the last decade, researchers have begun to develop new methods for 3-D face recognition. This thesis focuses on 3-D face recognition using the one- and two-dimensional Discrete Cosine Transform (DCT) . A feature ranking based dimensionality reduction strategy is introduced to select the DCT coefficients that yield the best classification accuracies. Two forms of 3-D representation are used: point cloud and depth map images. These representations are extracted from the original VRML files in a face database and are normalized during the extraction process. Classification accuracies exceeding 97% are obtained using the point cloud images in conjunction with the 2-D DCT.
| Identifer | oai:union.ndltd.org:siu.edu/oai:opensiuc.lib.siu.edu:theses-1045 |
| Date | 01 January 2009 |
| Creators | Hantehzadeh, Neda |
| Publisher | OpenSIUC |
| Source Sets | Southern Illinois University Carbondale |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses |
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