Retinal verification using point set matching

In this paper, we propose a new retinal verification method based on point set matching. First, image processing techniques are applied to segment the retinal image's optic disc. The blood vessel map is generated inside the segmented optic disc region. We observe that optic disc region blood vessels are more stable and exhibit unique variation for a particular person. The edge map of the optic disc blood vessels is computed and used as the feature for similarity measurement. The partial Hausdorff measure is used to compute similarity measure between two edge based feature maps. Experimental results on a set of retinal images in the publicly available VARIA database show promising verification (FAR, FRR and EER) performance.

[1]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Ron Shonkwiler,et al.  An Image Algorithm for Computing the Hausdorff Distance Efficiently in Linear Time , 1989, Inf. Process. Lett..

[3]  Ron Shonkwiler Computing the Hausdorff Set Distance in Linear Time for Any L_p Point Distance , 1991, Inf. Process. Lett..

[4]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Sharath Pankanti,et al.  An identity-authentication system using fingerprints , 1997, Proc. IEEE.

[6]  Dexin Zhang,et al.  Personal Identification Based on Iris Texture Analysis , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[8]  Manuel G. Penedo,et al.  Personal authentication using digital retinal images , 2006, Pattern Analysis and Applications.

[9]  Xu Cheng,et al.  The blood vessel recognition of ocular fundus , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[10]  Anil K. Jain,et al.  Performance evaluation of fingerprint verification systems , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Manuel G. Penedo,et al.  Biometric authentication using digital retinal images , 2006 .

[12]  Anil K. Jain,et al.  Validating a Biometric Authentication System: Sample Size Requirements , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Andrea F. Abate,et al.  2D and 3D face recognition: A survey , 2007, Pattern Recognit. Lett..

[14]  Daniel P. Lopresti,et al.  Forgery Quality and Its Implications for Behavioral Biometric Security , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  N.B. Puhan,et al.  Iris recognition on edge maps , 2007, 2007 6th International Conference on Information, Communications & Signal Processing.

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

[17]  N.A. Rahman,et al.  Retinal Identification , 2008, 2008 Cairo International Biomedical Engineering Conference.

[18]  Ahmed S. Fahmy,et al.  Fast Localization of the Optic Disc Using Projection of Image Features , 2010, IEEE Transactions on Image Processing.

[19]  L. Latha,et al.  EFFECTUAL HUMAN AUTHENTICATION FOR CRITICAL SECURITY APPLICATIONS USING RETINAL IMAGES , 2010 .

[20]  Stephen A. Davis,et al.  Retina Verification System Based on Biometric Graph Matching , 2013, IEEE Transactions on Image Processing.

[21]  Ganapati Panda,et al.  Hausdorff symmetry operator towards retinal blood vessel segmentation , 2014, 2014 19th International Conference on Digital Signal Processing.