Design and Analysis of Detection and Rectification of Distorted Fingerprints

Abstract

In this paper, we encouraged novel algorithms to determine and correct skin distortion centered on a single fingerprint image. Distortion detection is demonstrated as a two-class classification situation, for which the registered ridge orientation map and period map of a fingerprint are precious because the feature vector and a SVM classifier is knowledgeable to behave the classification mission. Distortion rectification (or equivalently distortion field estimation) is analyzed as a regression quandary, the place the input is a distorted fingerprint and the output is the distortion area. To simplify this difficulty, a database (referred to as reference database) of various distorted reference fingerprints and matching distortion fields is constructed within the offline stage, and then within the on-line stage, the closest neighbor of the input fingerprint is deliberate within the reference database and the an identical distortion area is used to change (Convert) the input fingerprint right into a normal fingerprints.