摘要
Fingerprint identification and recognition are considered popular technique in many security and law enforcement applications. The aim of this paper is to present a proposed authentication system based on fingerprint as biometric type, which is capable of recognizing persons with high level of confidence and minimum error rate. The designed system is implemented using Matlab 2015b and tested on a set of fingerprint images gathered from 90 different persons with 8 samples for each using Futronic’s FS80 USB2.0 Fingerprint Scanner and the ftrScanApiEx.exe program. An efficient image enhancement algorithm is used to improve the clarity (contrast) of the ridge structures in a fingerprint. After that core point and candidate core points are extracted for each Fingerprint image and feature vector have been extracted for each point using filterbank_based algorithm. Also, for the matching the KNN neural network was used. In addition, the matching results were calculated and compared to other papers using some performance evaluation factors. A threshold has been proposed and used to provide the rejection for the fingerprint images that does not belong to the database and the experimental results show that the KNN technique have a recognition rate equal to 93.9683% in a threshold equal to 70%.
Fingerprint identification and recognition are considered popular technique in many security and law enforcement applications. The aim of this paper is to present a proposed authentication system based on fingerprint as biometric type, which is capable of recognizing persons with high level of confidence and minimum error rate. The designed system is implemented using Matlab 2015b and tested on a set of fingerprint images gathered from 90 different persons with 8 samples for each using Futronic’s FS80 USB2.0 Fingerprint Scanner and the ftrScanApiEx.exe program. An efficient image enhancement algorithm is used to improve the clarity (contrast) of the ridge structures in a fingerprint. After that core point and candidate core points are extracted for each Fingerprint image and feature vector have been extracted for each point using filterbank_based algorithm. Also, for the matching the KNN neural network was used. In addition, the matching results were calculated and compared to other papers using some performance evaluation factors. A threshold has been proposed and used to provide the rejection for the fingerprint images that does not belong to the database and the experimental results show that the KNN technique have a recognition rate equal to 93.9683% in a threshold equal to 70%.
