A Novel Retinal Recognition System for Pathological Retina To Enhance Security

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Biometric acknowledgment gives a characteristic and solid answer for the issue of individual distinguishing proof. One of the biometric ID framework utilized with high exactness is the retinal verification as a result of its many-sided quality in spoofing. However in these frameworks, acknowledgment rate is enormously influenced by the vasculature multifaceted nature of retinal images. This vascular example turns out to be exceptionally perplexing in sick retinal pictures because of the nearness of obsessive signs. In this manner, we require a computerized productive calculation which can evacuate these abnormalities before matching and decision making. The proposed novel hybrid clustering algorithm Adaptive Weighted Neighbour (AWN) Classification Algorithm classifies the input retinal image based on the features extracted and match the features with the trained features. The proposed feature vector consists of blood vessel structure, bifurcation points, bifurcation angles and vessel width. Results from the AWN are compared with state of the art classifier. It enables high security, good performance and greater accuracy. Also it provides better FAR, FRR and decreases the error rate.

     

     


  • Keywords


    Biometric system, Blood vessels, identification, Retinal images, identification methods.

  • References


      [1] Fatima, Joddat, Adeel M. Syed, and M. Usman Akram. "A secure personal identification system based on human retina." In Industrial Electronics and Applications (ISIEA), IEEE Symposium on, pp. 90-9, 2013.

      [2] Hamid, Larry. "Biometric technology: not a password replacement, but a complement." Biometric Technology, no. 6, pp. 7-10, 2015.

      [3] Modarresi, Morteza, and Iman Sheikh Oveisi. "A Contourlet Transform Based for Features Fusion in Retina and Iris Multimodal Biometric System." In Biometric Authentication, pp. 75-90. 2014.

      [4] Patwari, Manjiri B., Ramesh R. Manza, Yogesh M. Rajput, Manoj Saswade, and Neha Deshpande. "Personal identification algorithm based on retinal blood vessels bifurcation." In Intelligent Computing Applications (ICICA), pp. 203-207, 2014.

      [5] Ahmed, M. Islamuddin, M. Ashraful Amin, Bruce Poon, and Hong Yan. "Retina based biometric authentication using phase congruency."International Journal of Machine Learning and Cybernetics Vol.5, no. 6, pp. 933-945, 2014.

      [6] Waheed, Zahra, M. Usman Akram, Amna Waheed, Muazzam A. Khan, Arslan Shaukat, and Mazhar Ishaq. "Person identification using vascular and non-vascular retinal features." Computers & Electrical Engineering, 2016.

      [7] Usher, David, Yasunari Tosa, and Marc Friedman. "Simultaneous Capture of Iris and Retina for Recognition." Encyclopedia of Biometrics, pp.1401-1407, 2015.

      [8] Frucci, Maria, Daniel Riccio, Gabriella Sanniti di Baja, and Luca Serino. "Severe: Segmenting vessels in retina images." Pattern Recognition, 2015.

      [9] Bartocha, Anna, Emil Saeed, Piotr Wachulec, and Khalid Saeed. "Retinal Feature Extraction with the Influence of Its Diseases on the Results." InApplied Computation and Security Systems, pp. 37-48, 2015.

      [10] Koukounis, Dimitris, Christos Ttofis, Agathoklis Papadopoulos, and Theocharis Theocharides. "A high performance hardware architecture for portable, low-power retinal vessel segmentation." INTEGRATION, the VLSI journal Vol.47, no. 3, pp.377-386, 2014.

      [11] Drozd, Radek, Josef Hájek, and Martin Drahanský. "An algorithm for retina features extraction based on position of the blood vessel bifurcation." InBiometric Recognition, Springer Berlin Heidelberg, pp. 308-315, 2012.

      [12] Unar, J. A., Woo Chaw Seng, and Almas Abbasi. "A review of biometric technology along with trends and prospects." Pattern recognition Vol.47, no. 8, pp.2673-2688, 2014.

      [13] Seto, Yoichi. "Retina recognition." Encyclopedia of biometrics, pp.1321-1323, 2015.

      [14] Dehghani, Amin, Zeinab Ghassabi, Hamid Abrishami Moghddam, and Mohammad Shahram Moin. "Human recognition based on retinal images and using new similarity function." EURASIP Journal on Image and Video Processing, no. 1, pp.1-10, 2013.

      [15] Panchal, Parth, Ronak Bhojani, and Tejendra Panchal. "An Algorithm for Retinal Feature Extraction Using Hybrid Approach." Procedia Computer Science Vol.79, pp. 61-68, 2016.

      [16] Meng, Weizhi, Duncan S. Wong, Steven Furnell, and Jianying Zhou. "Surveying the development of biometric user authentication on mobile phones." Communications Surveys & Tutorials, IEEE Vol.17, no. 3, pp.1268-1293, 2013.

      [17] Ashokkumar, S., and K. K. Thyagharajan. "Retina biometric recognition in moving video stream using visible spectrum approach." In Green Computing, Communication and Conservation of Energy (ICGCE), pp. 180-187, 2013.

      [18] Waheed, Zahra, Amna Waheed, and M. Usman Akram. "A robust non-vascular retina recognition system using structural features of retinal image." IEEE, pp. 101-105, 2016.

      [19] Borah, Tripti Rani, Kandarpa Kumar Sarma, and Pran Hari Talukdar. "Retina recognition system using adaptive neuro fuzzy inference system." InComputer, Communication and Control (IC4), pp. 1-6, 2015.

      [20] Chihaoui, Takwa, Rostom Kachouri, Hejer Jlassi, Mohamed Akil, and Kamel Hamrouni. "Human identification system based on the detection of optical Disc Ring in retinal images." In Image Processing Theory, Tools and Applications (IPTA), pp. 263-267, 2015.

      [21] Lajevardi, Seyed Mehdi, Arathi Arakala, Stephen A. Davis, and Kathy J. Horadam. "Retina verification system based on biometric graph matching.", IEEE Transactions on Image Processing, Vol.22, no. 9, pp.3625-3635, 2013.

      [22] Villalobos-Castaldi, Fabiola M., and Ernesto Suaste-Gómez. "A new spontaneous pupillary oscillation-based verification system." Expert Systems with Applications, Vol.40, no. 13, pp.5352-5362, 2013.

      [23] Grulkowski, Ireneusz, Jonathan J. Liu, Jason Y. Zhang, Benjamin Potsaid, Vijaysekhar Jayaraman, Alex E. Cable, Jay S. Duker, and James G. Fujimoto. "Reproducibility of a long-range swept-source optical coherence tomography ocular biometry system and comparison with clinical biometers."Ophthalmology Vol.120, no. 11, pp. 2184-2190, 2013.

      [24] Castaldi, Fabiola M. Villalobos, Edgardo M. Felipe-Riveron, and Ernesto Suaste Gómez. "A new retinal recognition system using a logarithmic spiral sampling grid." In Pattern Recognition, pp. 241-250, 2014.

      [25] Hao, Hao, D. Krishna Kumar, Behzad Aliahmad, Che Azemin, Mohd Zulfaezal, and Ryo Kawasaki. "Using color histogram as the trait of retina biometric." In Biosignals and Biorobotics Conference (BRC), pp. 1-4, 2013.


 

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Article ID: 17621
 
DOI: 10.14419/ijet.v7i3.12.17621




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