ANITHA L ROLL NO :4 M.TECH[CSE]

LITERATURE SURVEY PROPOSED SYSTEM PERFORMANCE STUDY INTRODUCTION OBJECTIVE

 Biometrics  “Automated measurement of Physiological and/or behavioral characteristics to determine or authenticate identity” Retina scan:Measures unique characteristics of the retina. ◦ Blood vessel patterns ◦ Vein patterns

An automatic retina verification framework based on  biometric graph matching algorithm.

 1. High performance system with features obtained from human retinal images:  blood vessel segmentation  Feature generation  Feature matching Experiments done with 60 images from DRIVE and STARE Databases. Average accuracy:99%

Blood segmentation and masking: (a) vessels pattern, (b) region of interest of vessels images around OD.

 2. Retinal Verification Using a Feature Points-Based Biometric pattern  Set of feature points reprsenting landmarks in retinal vessel tree.  Done with pattern extraction and matching  Deep analysis of similarity metrics performance is presented.

 Experiment done with a set of 90 images used from VARPA database  Execution time:155ms  Advantage: good confidence band even in distortion/illumination

 3. Personal authentication using digital retinal images  Matching Process:  Feature Points:  Finally pairs of sets will be matched inorder to get an accurate and reliable similarity measure for authentication

 4. Retinal Fundus Biometric Analysis for Personal Identifications  Blood vessel detection  Bifurcation point extraction Similarity score evaluated Experiment Results: Threshold=70% FAR & FRR 0%

 5. A robust person authentication based on score level fusion of left and right irises and retinal features  Multimodal biometric system.ie, using both iris and retina detection  More reliable and precise than single biometric systems

 6. Detection of blood vessels in retinal images using 2-D Matched filters  Detection of blood vessels  Design of matched filters(piecewise linear segments of blood vessels).

 Advantage: good performance in analyzing fluorescein angiogram images of the retina as well.  Disadvantage: Long time taken to run in ordinary PC because of large convolution kernel.

 7. Retinal Vessel Segmentation using 2-D Morlet Wavelet and Supervised Classification  Automated segmentation of vasculature in retinal images  Morlet Transform to enhance vessel contrast and filters out noise DRIVE & STARE databases used here. More accuracy on STARE.

 8.Retina Verification System Based on Biometric Graph Matching:  Retinal image enhancement  Retinal feature extraction  Retina graph  Biometric graph matching algorithm

Training set Testing set Retinal Feature extraction Retinal Feature extraction BGM Retinal Verification KDE And Threshold Selection

Retinal raw image from VARIA database normalized retinal image using histogram equalization, retinal image after applying 6 Matched filters

FMRThresholdFMRFN MR EER FNMR FNMR TRAINING SET TESTING SET

 An automatic retinal verification system has been presented based on the Biometric Graph Matching algorithm.  A spatial graph was generated from this skeleton of retinal vessels and it was used as an input to the BGM algorithm.  Experimental results indicated that the KDE model was a good fit to the data.

 The future work in this area will include optimising the registration algorithm to increase speed.  This research also seeks to test the verification system on larger retina databases with high quality color images and multiple samples, when such become available.

  [1] H. Farzin, H. Abrishami-Moghaddam, and M. S. Moin, “A novel retinal identification system,” EURASIP J. Adv. Signal Process., vol. 2008,pp. 1–10, Apr   [2] M. Ortega, M. G. Penedo, J. Rouco, N. Barreira, and M. J. Carreira,“Retinal verification using a feature points-based biometric pattern,” EURASIP J. Adv. Signal Process., vol. 2009, pp. 1–13, Mar   [3] C. Mariño, M. G. Penedo, M. Penas, M. J. Carreira, and F. González,“Personal authentication using digital retinal images,” J. Pattern Anal.Appl., vol. 9, no. 1, pp. 21–33,   [4] V. Bevilacqua, L. Cariello, D. Columbo, M. D. Fabiano, M. Giannini, G. Mastronardi, and M. Castellano, “Retinal fundus biometric analysis for personal identifications,” in Proc. Int.Conf. Intell. Comput., Sep. 2008, pp. 1229–1237.   [5] L. Latha and S. Thangasamy, “A robust person authentication system based on score level fusion of left and right irises and retinal features,” Procedia Comput. Sci., vol. 2, pp. 111–120,

 [6] S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum,“Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Trans. Med. Imag., vol. 8, no. 3, pp. 263–269, Sep   [7] J. Soares, J. Leandro, R. Cesar, H. Jelinek, and M. Cree, “Retinal vessel segmentation using the 2-d Gabor wavelet and supervised classification,” IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1214–1222, Sep   [8] R. Hill, Biometrics: Personal Identification in Networked Society. New York, NY, USA: Springer-Verlag, 1999, ch. 6, pp. 123–141.   [9]Seyed Mehdi Lajevardi, Member, IEEE, Arathi Arakala, Stephen A. Davis, and Kathy J. Horadam “Retina Verification System Based on BiometricGraph Matching” IEEE Transactions on image processing, vol. 22, NO. 9, September 2013   [10] (2006). VARPA. Varpa Retinal Images for Authentication Database.  [Online]. Available: 