Abstract. Singular value decomposition (SVD) is one of the most important and useful factorizations in linear algebra. We describe how SVD is applied to problems involving image processing-in particular, how SVD aids the calculation of so-called eigenfaces, which provide an efficient representation of facial images in face recognition. Although the eigenface technique was developed for ordinary grayscale images, the technique is not limited to these images. Imagine an image where the different shades of gray convey the physical threedimensional structure of a face. Although the eigenface technique can again be applied, the problem is finding the three-dimensional image in the first place. We therefore also show how SVD can be used to reconstruct three-dimensional objects from a two-dimensional video stream.

In this article, we show that the ideas behind the eigenface technique can be easily extended to the OCR problem by using multiple image classes. In addition, we also describe a modification to the standard Euclidean distance measure used for recognition which gives very good results in this context.

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I declare that this dissertation is my own, unaided work. It is being submitted for the degree of Master of Science in Engineering in the University of Cape Town. It has not been submitted before for any degree or examination in any other university.

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