An Iris Recognition System Using Deep convolutional Neural Network

Abstract

Machine learning rises in varied areas of computer science. A deep conventional neural network is powerful visual models of machine learning. We tend to present robustness and effective structure for the iris recognition system. The image first pass through these stages: enhancing the image quality, determine the iris and pupil center and radius for iris segmentation, converting the image from the Cartesian coordinates to the polar coordinates to reduce the time of processing. The proposed system is named IRISNet that extracting the feature and classifying them automatically without any domain knowledge. The architecture of IRISNet consists of Convolutional Neural Network layers for extract feature and Softmax layer to classify these features into N classes, for training CNN the backpropagation algorithm and Adam optimization method are used for updating the weights and learning rate, respectively. The performance of the proposed system was evaluated using IITD V1 iris database. The results obtained from the proposed system outperform supervised classification model (SVM, KNN, DT, and NB). The identification rate 97.32% and 96.43% for original and normalized images respectively. The recognition time per person is less than one second.

An iris segmentation using harmony search algorithm and fast circle fitting with blob detection

“…Omran et al [19], to localize the pupil, authors enhance the image by using histogram equalization and median filter. Gamma correction and blurring disk filter is used to determine the pupil boundary.…”

Performance Analysis of Feature Extraction Approach: Local Binary Pattern and Principal Component Analysis for Iris Recognition system

“…PCA guides how to reduce a high dimension data to a lower dimensional data to explore only the sufficient and important features. Extraction of the important features corresponding to the available data is the one of the major aspect of PCA [26]. During this, dimensionality of a data consisting of many correlated variables will get minimized.…”

Towards more accurate and efficient human iris recognition model using deep learning technology

“…Daugman [10] introduced the iris identification model based Gabor filter and Hamming distance using different iris dataset with high accuracy and speed. Omran and AlShemmary [11] has proposed iris recognition system called IrisNet for extracting  ISSN: 1693-6930 the attributes of the Indian Institute of Technology Delhi (IITD) V1 iris dataset and classifying them automatically. The structure of the IrisNet comprised of different CNN layers and trained using backpropagation algorithm and Adam optimizer.…”