نظام تعريف البصمة باستخدام الخوارزمية الجينية == Fingerprint Identification SystemUsing Genetic Algorithm
Author name:
ذكرى محمد عبد
Supervisor name:
ضياء عبد الحسين جمعة الزبيدي
General topic:
Computer Science
Specific topic:
Computer Science
Degree:
Master
University:
Mustansiriyah University - College Of Science - Department Of Computer
Language:
English
University location:
Baghdad
First pages:
28T834 - p.pdf
Abstract:
Identification system has been widely covered by many researchers using different methods to reach to the desired goal of the best and accurate security method. Existing security methods rely on knowledge based on approaches like password or token based on approaches like access cards. Such methods are not very secure, biometrics such as fingerprint, face and voice offer are means of personal identification and provide increased security because they rely on characteristics are existed in us. In this thesis, method of fingerprint identification system is introduced. The proposed system has used 196 fingerprint image back to the 28 individual, 140 image from them has been used for training and 56 image has been used for testing. Discrete Cosine Transform has been used to extract distinctive features from fingerprint image and genetic algorithm (GA) has been used as features selection technique. Genetic algorithm has helped to produce GA filter in order to select subset of features out of DCT. When testing the proposed system by using two type of statistical pattern recognition, (Probabilistic Neural Network and K - Nearest Neighbor) have found the identification rate reaching to 91% with superiority K - Nearest Neighbor algorithm in reaching this rate with the use of less number of features (68 feature). This rate has emboldened on attempting using more than one filter of genetic algorithm, the result reached to 98% as identification rate in two classifiers with more reduction in number features. The proposed system has been tested before using genetic algorithm and identification rate has reached to 89%. A code for the proposed identification system has been written with the use of matlabversion(7.6).As the specifications of the computer, which is used : Operating System : Windows 7, Processor : 2GHz, Memory : 3072MB
