A simple and stable method of creating fingerprint features with image rotation

Abstract

The main purpose of the classification of fingerprints is to devise a formula by which a given collection of fingerprints can be tracked and registered. To accelerate the system for classifying fingerprints, it is necessary to utilize fingerprint image characteristics and avoid the different fingerprint forms arising from fingerprint rotation. This paper presents a simple, new approach to the extraction of characteristics from fingerprint images. The proposed method demonstrates that, for a given image, the features remain constant even after being subjected to a wide range of rotations; thus, it creates an array of characteristics which can be used to identify a person from their fingerprint. To achieve this goal, a basic hit-and-miss operation with different structural components is used to detect and count various features in the fingerprint picture; these features are directly identified based on the texture of the fingerprint. The chosen features are used to index the finger image by generating a frequency of occurrences for each one, such that every fingerprint is represented as a vector of these features. The application of the proposed method shows efficient utilization of execution time and memory usage.