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

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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.
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