An efficient approach for neural network based fingerprint recognition by using core, delta, ridge bifurcation and Minutia

Abstract

Fingerprint recognition refers to the automated method of verifying a match between two human fingerprints. Fingerprints are one of many forms of biometrics used to identify individuals and verify their identity. In this paper we create a neural network algorithm for fingerprint recognition that is using the three basic patterns of fingerprint ridges are the arch, loop, and whorl. We know that an arch is a pattern where the ridges enter from one side of the finger, rise in the center forming an arc, and then exit the other side of the finger. The loop is a pattern where the ridges enter from one side of a finger to exit from the same side they enter. In the whorl pattern, ridges form circularly around a central point on the finger. First we design a supervised neural network for any fingerprint images by using three basic pattern then algorithm outputs show the recognition result. By this method, we improve the recognition result and comparison with other fingerprint image and also it is very useful to overcome the problem of finding number of criminals in the crime. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.