@article{MAKHILLJEAS201914117314,
    title = {Authentication on Smartphones based Iris Recognition},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {1},
    pages = {224-232},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.224.232},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.224.232},
    author = {Rana Jassim,Naji Mutar and},
    keywords = {MICHE-I dataset,circular distribution of angles,iris segmentation,Smartphones,Zernike moments,KNN},
    abstract = {The prevalent utilization of smartphones with the connectivity of the internet has led to sensitive data
storage and transmission. This has increased the necessity to implement reliable user authentication on
smartphones for preventing an attacker from reaching such data. The biometrics indicates the science of
individual&#146;s recognition depending on their behavioral and biological traits. In order to perform reliable
verification in smartphones, we briefly discuss the suitability of using the iris texture for biometric recognition
in smartphones. In this study, we propose a new and robust iris segmentation method based on the circular
distribution of angles to localize the iris boundary which applied on an eye noisy image passed through
the pre-processing operation. For feature extraction, Zernike moments is used. After feature extraction, matching
is performed by KNN. The proposed system has been experiment on the MICHE-I (Mobile iris Challenge
Evaluation) iris dataset (Samsung Galaxy S4 (SG4) iris image database), featuring subjects captured indoor and
outdoor under controlled and uncontrolled conditions by means of built-in cameras aboard three among the
most diffused smartphones/tablets on the market. The evaluation of the obtained results show that the
developed system can provide successfully iris recognition on the tested noisy imagesunder difficult
environments compared to previous techniques and we have achieved 80% Av. accuracy rate with SG4.}
    }