Recognition and Refinement of Distorted Fingerprints Using Markov Mapping

Abstract

Distortion rectification (or equivalently distortion field estimation) is viewed as a regression problem, where the input is a distorted fingerprint and the output is the distortion field. To solve this problem, a database (called reference database) of various distorted reference fingerprints and corresponding distortion fields is built in the offline stage, and then in the online stage, the nearest neighbor of the input fingerprint is found in the reference database and the corresponding distortion field is used to transform the input fingerprint into a normal one. One of the open come back outs in fingerprint confirmation is that the lack of strength against image quality degradation. Poor quality pictures end in specious and missing options, so degrading the performance of the general system. Consequently, it's very important to get a fingerprint reputation process to help approximate the quality as well as validity in the harnessed fingerprint photographs. In addition the particular variable distortion regarding fingerprints is one of the major causes for false non-match. Whilst this matter effects most fingerprint accepted purposes, it's especially unsafe in adverse recognition purposes, such as check out record as well as reduplication purposes.