Comparative study on face recognition techniques: Principal Component Analysis and Linear Discriminant Analysis
dc.contributor.author | Peprah, Frank | |
dc.date.accessioned | 2016-10-17T10:09:11Z | |
dc.date.accessioned | 2023-04-19T13:51:09Z | |
dc.date.available | 2016-10-17T10:09:11Z | |
dc.date.available | 2023-04-19T13:51:09Z | |
dc.date.issued | NOVEMBER 2015 | |
dc.description | A thesis submitted to The Institute of Distance Learning, KNUST, in partial fulfilment of the requirements for the award of Master of Science Degree in Information Technology, 2015 | en_US |
dc.description.abstract | Face Recognition System employs a variety of feature extraction (projection) techniques which are grouped into Appearance-Based and Feature-Based. In a vast majority of the studies undertaken in the field of Face Recognition special attention is given to the Appearance-Based Methods which represent the dominant and most popular feature extraction technique used. Even though a number of comparative studies exist, researchers have not reached consensus within the scientific community regarding the relative ranking of the efficiency of the appearance-based methods (LDA, PCA etc) for face recognition task. This paper studied two appearance-based methods (LDA, PCA) separately with three (3) distance metrics (similarity measures) such as Euclidean distance, City Block & Cosine to ascertain which projection-metric combination was relatively more efficient in terms of time it takes to recognise a face. The study considered the effect of varying the image data size in a training database on all the projection-metric methods implemented. LDA-Cosine Distance Metric was consequently ascertained to be the most efficient when tested with two separate standard databases (AT & T Face Database and Indian Face Database). It was also concluded that LDA outperformed PCA. | en_US |
dc.description.sponsorship | KNUST | en_US |
dc.identifier.uri | https://ir.knust.edu.gh/handle/123456789/9267 | |
dc.language.iso | en | en_US |
dc.title | Comparative study on face recognition techniques: Principal Component Analysis and Linear Discriminant Analysis | en_US |
dc.type | Thesis | en_US |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- FRANK PEPRAH-MSc-2015.pdf
- Size:
- 3.36 MB
- Format:
- Adobe Portable Document Format
- Description:
- Full Thesis