An Efficient Supervised Approach for Retinal Person Identification using Zernike Moments

moments map images using orthogonal basis functions. These moments have the advantages of rotation invariance, robustness and minimum information redundancy. In this paper, we focus on distinguishable pattern analysis of the retinal fundus images for person identification using Zernike moments. These moments are used to form 11-D feature vectors and k-nearest neighbor (kNN) classifier is used for person identification on publicly available DRIVE and STARE databases. This method outperforms all the existing methods with accuracy of 100% and 98.64% on DRIVE and STARE databases respectively. Its smaller dimension of feature vector, simplicity and robustness make this method suitable for real-time retinal person identification scheme. General Terms - Biometrics, security, pattern recognition.

[1]  Ratnadeep R. Deshmukh,et al.  Analysis of Retina Recognition by Correlation and Covariance Matrix , 2010, 2010 3rd International Conference on Emerging Trends in Engineering and Technology.

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

[3]  Alireza Khotanzad,et al.  Rotation invariant pattern recognition using Zernike moments , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[4]  M. Usman Akram,et al.  Retinal recognition: Personal identification using blood vessels , 2011, 2011 International Conference for Internet Technology and Secured Transactions.

[5]  Ryszard S. Choras,et al.  Retina recognition for biometrics , 2012, Seventh International Conference on Digital Information Management (ICDIM 2012).

[6]  S. Qamber,et al.  Personal identification system based on vascular pattern of human retina , 2012, 2012 Cairo International Biomedical Engineering Conference (CIBEC).

[7]  Arun Ross,et al.  An introduction to biometric recognition , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  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.

[9]  Wafa Barkhoda,et al.  Retina identification based on the pattern of blood vessels using fuzzy logic , 2011, EURASIP J. Adv. Signal Process..

[10]  Mansour Vafadoost,et al.  Wavelet based retinal recognition , 2007, 2007 9th International Symposium on Signal Processing and Its Applications.

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

[12]  S. Reza Hadianamrei,et al.  Retinal Identification System Based on the Combination of Fourier and Wavelet Transform , 2012 .

[13]  L. Latha,et al.  Efficient Approach for Retinal Biometric Template Security and Person Authentication using Noninvertible Constructions , 2013 .

[14]  Pascale Massin,et al.  Automatic detection of microaneurysms in color fundus images , 2007, Medical Image Anal..

[15]  M. Teague.  Image analysis via the general theory of moments , 1980 .