svd_fingerprint, a MATLAB code which reads a file containing a fingerprint image and uses the singular value decomposition (SVD) to compute and display a series of low rank approximations to the image.
In MATLAB, images can be thought of as numeric arrays (although you do have to convert them from the uint8 numeric format used for images to the double format used for numeric arrays.)
Therefore, an MxN image A has an SVD decomposition A = U*S*V'.
For any 1 <= R <= min(M,N), a low rank approximation to A is formed by
Ar = U(1:m,1:r) * S(1:r,1:r) * V(1:n,1:r)';
Properties of the SVD guarantee that Ar is the best possible rank R approximation
to the data in A. This means it is often possible to get a good approximation
to A using much less data.
Regarding a fingerprint image as collection of column vectors, we can apply this technique. Fingerprints can be difficult to compress, since they have a great deal of fine variation and detail.
The computer code and data files described and made available on this web page are distributed under the MIT license
svd_fingerprint is available in a MATLAB version.
fingerprints, a dataset directory which contains some images of fingerprints.
svd_basis, a MATLAB code which applies the singular value decomposition (svd) to a collection of data vectors, extracting dominant modes;
svd_test, a MATLAB code which demonstrates the singular value decomposition for a simple example.
svd_gray, a MATLAB code which reads a gray scale image, computes the singular value decomposition (svd), and constructs a series of low rank approximations to the image.
svd_snowfall, a MATLAB code which reads a file containing historical snowfall data and analyzes the data with the singular value decomposition (svd).