Comparative study on face recognition techniques: Principal Component Analysis and Linear Discriminant Analysis

Simple item page
dc.contributor.authorPeprah, Frank
dc.date.accessioned2016-10-17T10:09:11Z
dc.date.accessioned2023-04-19T13:51:09Z
dc.date.available2016-10-17T10:09:11Z
dc.date.available2023-04-19T13:51:09Z
dc.date.issuedNOVEMBER 2015
dc.descriptionA 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, 2015en_US
dc.description.abstractFace 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.sponsorshipKNUSTen_US
dc.identifier.urihttps://ir.knust.edu.gh/handle/123456789/9267
dc.language.isoenen_US
dc.titleComparative study on face recognition techniques: Principal Component Analysis and Linear Discriminant Analysisen_US
dc.typeThesisen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
FRANK PEPRAH-MSc-2015.pdf
Size:
3.36 MB
Format:
Adobe Portable Document Format
Description:
Full Thesis
Download
License bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
license.txt
Size:
1.73 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
College of Science