The Compression of Fingerprints Using Sparse Representation

Abstract

Legal matters, such as the investigation of a crime, rely heavily on fingerprint analysis. A fingerprint image, on the other hand, contains a staggering quantity of information. Because of this, we must limit the amount of data it contains. We'll need a powerful image compression method to do this. There are a plethora of methods for compressing images. [1] Images of finger prints are not always perfect quality. It's possible that skin and impression situations will damage or corrupt them. Thus, image enhancements prior to the extraction of minutiae, [5] various procedures are used to ensure a more accurate estimation of their placements. In this article, we'll look at a few different ways to compress fingerprints. This section concludes with an approach for compressing fingerprints. Sparsely populated. Our next step is to build a database of preconfigured fingerprint image patches. [3] Divide a fingerprint into small chunks called patches for a specific fingerprint. In order to obtain the, utilise the sparse representation approach the next step is to quantize the coefficients, and the final step is to encode them. Fingerprint images from three groups are tested in this experiment. The results of the tests show that our approach outperforms the competition in terms of compression efficiency. [7] The primary the minutiae is a feature that is used to compare two fingerprint images. As a result, the pre/post difference in minutiae There is some thought given to compression.