Research Article
An Efficient Approach for Neural Network Based Fingerprint Recognition by Using Core, Delta, Ridge Bifurcation and Minutia
@INPROCEEDINGS{10.1007/978-3-642-35615-5_57, author={Jitendra Sengar and Jasvinder Singh and Niresh Sharma}, title={An Efficient Approach for Neural Network Based Fingerprint Recognition by Using Core, Delta, Ridge Bifurcation and Minutia}, proceedings={Third International conference on advances in communication, network and computing}, proceedings_a={CNC}, year={2012}, month={12}, keywords={Image processing Minutia analysis Ridge analysis Pixel orientation}, doi={10.1007/978-3-642-35615-5_57} }- Jitendra Sengar
Jasvinder Singh
Niresh Sharma
Year: 2012
An Efficient Approach for Neural Network Based Fingerprint Recognition by Using Core, Delta, Ridge Bifurcation and Minutia
CNC
Springer
DOI: 10.1007/978-3-642-35615-5_57
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.