Document Type : Full Research Paper


1 MSc Graduate in Biomedical Engineering, Mashad University of Medical Sciences

2 Associate Professor, Biomedical Engineering Department, Electrical School, KN. Toosi University of Technology

3 Assistant Professor, Multimedia Systems Department, Faculty of Information Technology, Iran's Telecommunication Research Center



One of the most accurate techniques for human identification is based on the uniqueness of the retinal blood vessels pattern. In this paper, we present a new approach for human identification using retina image. This approach is insensitive to rotation, scaling and translation. The Fourier-Mellin transform coefficients and moments of the retinal image were used to extract the suitable features. To compensate the rotational effects caused by different relative positions of the retina scanner with respect to the eye, a rotation compensator was designed. For retinal image interpretation, the optic disc location was considered as a fixed and reference point. For its localization, the Haar wavelet and the Snakes model were used. The experimental results demonstrated an error rate close to zero for the proposed method. 


Main Subjects

[1]     جعفریانی هادی؛ بررسی هویت افراد بر اساس تصویر شبکیه؛ پایان نامه کارشناسی ارشد مهندسی پزشکی، دانشکده برق، دانشگاه صنعتی خواجه نصیرالدین طوسی، 1382.
[2]     جعفریانی هادی، ابریشمی مقدم حمید، معین محمدشهرام؛ بررسی هویت افراد بر اساس تصویر شبکیه؛ گزارش فنی، مرکز تحقیقات مخابرات ایران، 1382.
[3]     جعفریانی هادی، ابریشمی مقدم حمید، معین محمدشهرام؛ روشی جدید برای بررسی هویت افراد بر اساس تصویر شبکیه؛ مجموعه مقالات یازدهمین کنفرانس مهندسی پزشکی ایران، دانشگاه صنعتی امیرکبیر، بهمن 1382.
[4]     Lalonde M., Beaulieu M., Gagnon L., Fast and robust optic disc detection using pyramidal decomposition and hausdorff based template matching; IEEE Trans. Medical Imaging 2001; 20:1193–1200.
[5]     Guyton A.C., Hall J. E., Textbook of Medical Physiology, W. B. Saunders, 10th Ed., 2000.
[6]     Simon C., Goldstein I., Retinal Method of Identification; New York State Journal of Medicine 1936; 15.
[7]     Lowell J., Hunter A., Steel D., Basu A., Ryder R., Fletcher E., Kennedy L., Optic nerve head segmentation; IEEE Transactions on Medical Imaging 2004; 23(2):256-264.
[8]     Mendels F., Heneghan C., Thiran J. P., Identification of the Optic Disk Boundary in Retinal Images Using Active Contours; Proc. IMVIP 1999; 103-115.
[9]     Kass M., Witkin A., Terzopoulos D., Snakes: Active contour models; Int. Journal of Computer Vision 1987; 1(4):321-331.
[10] Samadini R. C., Adaptive Snakes: Control of Damping and Material Parameters; Proc. SPIE Geometric Methods in Computer Vision 1991; 1570:202–213.
[11] Yatagay T., Choji K., Saito H., Pattern classification using optical Mellin transform and circular photodiode array; Optical Communication 1981; 38(3):162-165.
[12] Ghorbel F., A complete invariant description for graylevel images by the harmonic analysis approach; Pattern Recognition Letters 1994; 54:1043-1051.
[13] Hu M.K., Pattern Recognition by Moment Invariant; Proc. of the IRE 1961; 1428.
[14] Image Science Institute, DRIVE: Digital Retinal Images for Vessel Extraction, Research/Databases/DRIVE/
[15] Daugman J.G., New methods in iris recognition; IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics 2007; 37(5):1167-1175.