Performance analysis of biometric recognition system based on palmprint

Abstract

Nowadays biometric authentication is one of the most preferred choice and in that also palmprint is becoming the most widely accepted technique because it can be captured easily and the algorithms can be implemented with ease. Detection of coarse lines in palm images is easy to the point that these lines can be revealed even by using a low determination camera. The integration of palmprint recognition with some other biometric recognition system does not require any special capture devices, so it can be done easily. Hence, this method suites best for individual verification. This research work is related to palmprint recognition in which the main aim is related to software based performance analysis of classifiers. The entire work is divided into three major phases namely palmprint pre-processing, feature extraction and accuracy estimation. Different functions and operators related to binarization, morphology and other operations are used for pre-processing; similarly feature extraction is carried out by using Frangi filter, Freak descriptor and FAST algorithm. After the final feature extraction procedure, classifiers are used to predict the accuracy in identifying right and wrong features for a given subject. This research work uses two classifiers namely discriminant analysis and K-nearest neighbor algorithm (KNN) where discriminant analysis shows 77% accuracy and KNN gives a better accuracy of 93%.