Face Recognition using Principal Component Analysis
| dc.contributor.author | Baah, Gyasi Silas | |
| dc.date.accessioned | 2014-01-13T15:46:23Z | |
| dc.date.accessioned | 2023-04-19T16:18:55Z | |
| dc.date.available | 2014-01-13T15:46:23Z | |
| dc.date.available | 2023-04-19T16:18:55Z | |
| dc.date.issued | 2013 | |
| dc.description | A thesis submitted to the Department of Mathematics, in partial fulfillment of the requirements for the award of degree of Master of Science Industrial Mathematics | en_US |
| dc.description.abstract | Face Recognition is the process of identification of a person by their facial image. This technique makes it possible to use the facial images of a person to authenticate him into a secure system, for criminal identification, for passport verification, surveillance, etc... Face recognition is a popular research area where there are different approaches studied in literature. In this thesis, a holistic Principal Component Analysis (PCA) based method, namely Eigenface method is studied and implemented on the Faces 94 database. This approach treats face recognition as a two-dimensional recognition problem. Face images are projected onto a face space that encodes best variation among known face images. The face space is defined by eigenface which are eigenvectors of the set of faces, which may not correspond to general facial features such as eyes, nose, and lips. Face will be categorized as known or unknown face after matching with the present database. Experimental results in this thesis showed that an accuracy of 98.8158% was achieved. The variable reducing theory of PCA accounts for the smaller face space than the training set of face. | en_US |
| dc.description.sponsorship | KNUST | en_US |
| dc.identifier.uri | https://ir.knust.edu.gh/handle/123456789/5543 | |
| dc.language.iso | en | en_US |
| dc.title | Face Recognition using Principal Component Analysis | en_US |
| dc.type | Thesis | en_US |
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