A secure personal identification system based on human retina

Biometrics are the personal physiological and behavioral characteristics which are mostly used for personal recognition. Today, biometric based security systems such as fingerprint, iris and face recognition are used everywhere especially in high security areas. Human retina, not same as iris, is another source of biometric system which provides the most reliable and stable means of authentication. In this paper, we present a new system based on vascular pattern of human retina which can be used for security purposes. The proposed algorithm consists of three stages; i.e. preprocessing, feature extraction and finally the matching process. In preprocessing, it extracts the vascular pattern from input retinal image using wavelets and multilayered thresholding technique. Second stage extracts all possible feature points and represents each feature point with a feature vector. The proposed system matches the template feature vectors and input image feature vector using Mahalanobis distance. The experimental results demonstrate that the proposed system performs retina recognition with high accuracy.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  Gary G. Yen,et al.  A Sorting System for Hierarchical Grading of Diabetic Fundus Images: A Preliminary Study , 2008, IEEE Transactions on Information Technology in Biomedicine.

[3]  M. Usman Akram,et al.  An automated system for colored retinal image background and noise segmentation , 2010, 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA).

[4]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

[5]  A Hoover,et al.  Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response , 1998, AMIA.

[6]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[7]  P. Tower The fundus oculi in monozygotic twins; report of six pairs of identical twins. , 1955, A.M.A. archives of ophthalmology.

[8]  Shehzad Khalid,et al.  Identification and classification of microaneurysms for early detection of diabetic retinopathy , 2013, Pattern Recognit..

[9]  Hamid Abrishami Moghaddam,et al.  A Novel Retinal Identification System , 2008, EURASIP J. Adv. Signal Process..

[10]  Xu Chen,et al.  The Identification and Recognition Based on Point for Blood Vessel of Ocular Fundus , 2006, ICB.

[11]  A.D. Hoover,et al.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response , 2000, IEEE Transactions on Medical Imaging.

[12]  Sharath Pankanti,et al.  Biometrics: Personal Identification in Networked Society , 2013 .

[13]  M. Goldbaum,et al.  Detection of blood vessels in retinal images using two-dimensional matched filters. , 1989, IEEE transactions on medical imaging.

[14]  Luminita Vasiu,et al.  Biometric Recognition - Security and Privacy Concerns , 2004, ICETE.

[15]  Hiroshi Fujita,et al.  Personal identification based on blood vessels of retinal fundus images , 2008, SPIE Medical Imaging.

[16]  Roberto Marcondes Cesar Junior,et al.  Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification , 2005, IEEE Transactions on Medical Imaging.

[17]  Sushma G. Thorat Locating the Optic Nerve in a Retinal Image Using the Fuzzy Convergence of the Blood Vessels , 2014 .

[18]  Manuel G. Penedo,et al.  Retinal Verification Using a Feature Points-Based Biometric Pattern , 2009, EURASIP J. Adv. Signal Process..

[19]  Vitoantonio Bevilacqua,et al.  Retinal Fundus Biometric Analysis for Personal Identifications , 2008, ICIC.

[20]  José Manuel Bravo,et al.  A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features , 2011, IEEE Transactions on Medical Imaging.

[21]  M. Usman Akram,et al.  Multilayered thresholding-based blood vessel segmentation for screening of diabetic retinopathy , 2011, Engineering with Computers.