Hema Rajini N, Chandra Prabha K, Department of Computer Science and Engineering, Alagappa Chettiar Government College of Engineering and Technology, Karaikudi, Tamil Nadu, India.
International Journal of Advanced Information and Communication Technology
Received On : April 12, 2020
Revised On : May 18, 2020
Accepted On : July 30, 2020
Published On : August 05, 2020
Volume 07, Issue 08
Pages : 115-118
Abstract
A inner knuckle print identification system has been designed and developed. This work presents a new approach to authenticate people according to their finger textures. This proposed method consists of three stages. They are preprocessing, feature extraction and matching. In the first stage, noise is suppressed using an image filtering. In the second stage, features are extracted by local line binary pattern. Artificial neural network and support vector machine are used to provide an efficient matching algorithm for inner knuckle print authentication. After matching, the algorithm returns the best match for the given fingerprint parameters. The use of inner knuckle print in biometric identification has been the most widely used authentication system. A classification with an accuracy of 89% and 97% has been obtained by support vector machine and artificial neural network classifier.
Keywords
Local line binary pattern; Feature extraction; Artificial neural network.
Cite this article
Hema Rajini N, Chandra Prabha K, “ Inner Knuckle Print Identification Using Artificial Neural Network, ” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp. 115-118, August. 2020.
Copyright
© 2020 Hema Rajini N, Chandra Prabha K. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.