Authentication by Palmprint Recognition using Phase-Difference Trained by Probability Neural Network

Abstract

In this paper we are using phase difference method for the purpose of extracting the features and for training and testing the extracted features Probability Neural Networks is used the extracted features are treated with PCA. Among the various methods available methods, PNN stands apart from the set because it improves the effective level of security by just increasing the number of hidden layers instead of changing the parameters, as in other systems. The basic reason why we use phase difference is that for obtaining high precision and efficiency (i.e.) we have two parts in phase difference namely the real and imaginary part, to reduce the system execution time we have chosen only the real part and processed with it. We have various techniques but we have chosen PCA method as a dimensionality reduction technique since it takes only the ROI that we have defined before into account. This project aims to provide an effective authentication system. The combination of PNN with phase difference for sure will be a cost effective, highly secure and a robust setup. The setup proves to be immune to illumination and other factors that happens to hinder performance during capture of the input image (palm-print).