Human Identification Model Considering Biometrics Features

In the medical field, brain classification is an effective technique for identifying a person through his print based on hidden biometrics of high specificity included in magnetic resonance images(MRI) brain, as this privacy strongly contributes to issue verification and identification person. paper, extracted from MRI obtained 50 healthy people, which were passed several pre-processing techniques order be used stage convolutional neural network model, among those pre-classification stages, data collection after extracting influential features each image, was linear discrimination analysis (LDA). The experimental results showed importance using LDA feature extraction adoption input K-NN CNN classifiers. classifiers proved successful if with help adopted. Where had ability classify accuracy 99%, 82% K-NN. final fingerprint relied mainly model's success classifying predicting remaining testing stage.