This manuscript is related to use of linear algebra called “SVD” to digital image processing. We investigate the use of SVD in digital image processing for image compression and face recognition. Singular value decomposition method can transpose a matrix into USV^t, which is use to refactoring a digital image in three matrices. The using of singular values of such refactoring allows us to represent the image with a smaller set of values, which can preserve useful features of the original image, but use less storage space in the memory, and achieve the image compression process. For image compression by SVD, we will do an experiment on a digital image with the help of MATLAB code. We will use different singular values for compression of image and evaluate different compression ratio and quality measurement for best compression. To perform face recognition with SVD, we treated the set of known faces as vectors in a subspace, called “facespace”, spanned by a small group of “basefaces”. The projection of a new image onto the baseface is then compared to the set of known faces to identify the face. All tests and experiments are carried out by using MATLAB as computing environment and programming language.
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