Face recognition is a popular research area where there are different
approaches studied in the literature. In this thesis, a holistic Principal
Component Analysis (PCA) based method, namely Eigenface method is
studied in detail and three of the methods based on the Eigenface method
are compared. These are the Bayesian PCA where Bayesian classifier is
applied after dimension reduction with PCA, the Subspace Linear
Discriminant Analysis (LDA) where LDA is applied after PCA and
Eigenface where Nearest Mean Classifier applied after PCA. All the
three methods are implemented on the Olivetti Research Laboratory
(ORL) face database, the Face Recognition Technology (FERET)
database and the CNN-TURK Speakers face database. The results are
compared with respect to the effects of changes in illumination, pose and
aging. Simulation results show that Subspace LDA and Bayesian PCA
perform slightly well with respect to PCA under changes in pose / however, even Subspace LDA and Bayesian PCA do not perform well
under changes in illumination and aging although they perform better
than PCA.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/1056738/index.pdf |
| Date | 01 January 2003 |
| Creators | Gul, Ahmet Bahtiyar |
| Contributors | Alatan, Aydin A. |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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