International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

Identification via Retinal Vessels Combining LBP and HOG

  • Received : 2023.03.05
  • Published : 2023.03.30
Download PDF
⟨ Previous Next ⟩

Abstract

With development of information technology and necessity for high security, using different identification methods has become very important. Each biometric feature has its own advantages and disadvantages and choosing each of them depends on our usage. Retinal scanning is a bio scale method for identification. The retina is composed of vessels and optical disk. The vessels distribution pattern is one the remarkable retinal identification methods. In this paper, a new approach is presented for identification via retinal images using LBP and hog methods. In the proposed method, it will be tried to separate the retinal vessels accurately via machine vision techniques which will have good sustainability in rotation and size change. HOG-based or LBP-based methods or their combination can be used for separation and also HSV color space can be used too. Having extracted the features, the similarity criteria can be used for identification. The implementation of proposed method and its comparison with one of the newly-presented methods in this area shows better performance of the proposed method.

Keywords

References

  1. C. Simon and I . Goldstein, "A New Scientific Method of Identification," New York State Journal of Medicine, Vol. 35, No. 18, pp. 901-906, September, 1935. 
  2. A Kumar, D Zhang, Hand geometry recognition using entropy-based discretization. IEEE Trans. Inf. Forensics Security.2(2), 181-187 (2007)  https://doi.org/10.1109/TIFS.2007.896915
  3. M. Tanaka and K. Tanaka: "An automatic technique for fundus-photograph mosaic and vascular net reconstruction." in MEDI NFO ' 80. Amsterdam. The Netherlands: North-Holland, 1980, pp. 116-120. 
  4. N. Katz, M. Sleightholm: "An image Processing System for automatic retina diagnosis." Proc. of SPIE., vol. 902, pp. 131 -137. 
  5. K. Akita and H. Kuga: "Pattern recognition of blood vessel networks in ocular fundus images." In IEEE Itzt. Workshop Phys. And fig . In Med Imaging, Mar. 15-18. 1982, pp. 436-441. 
  6. Hoover, V. Kouznetsova, and M. Goldbaum. "Locating blood vessels in retinal images by piece wise threshold probing of a matched filter response, '' I EEE Trms Medical imaging ,vol. 19. no. 3, pp. 203-210. March 2000.  https://doi.org/10.1109/42.845178
  7. H Farzin, H Abrishami Moghaddam, M-S Moin, A novel retinal identification system. EURASIP J Adv Signal Process.2008, 280635 (2008).doi:10.1155/2008/280635. 
  8. Z-W Xu, X-X Guo, X-Y Hu, X Cheng, The blood vessel recognition of ocular fundus, in Proceedings of the 4th International Conference on Machine Learning and Cybernetics(Guangzhou, 2005), pp. 4493-4498. 
  9. H Tabatabaee, A Milani-Fard, H Jafariani, A Novel Human Identifier SystemUsing Retina Image and Fuzzy Clustering Approach, in Proceedings of the2nd IEEE International Conference on Information and Communication Technologies(Damascus, 2006), pp. 1031-1036 
  10. M Shahnazi, M Pahlevanzadeh, M Vafadoost, Wavelet based retinal recognition, in Proceedings of the 9th IEEE International Symposium on Signal Processing and Its Applications(Sharjah, 2007), pp. 1-4. 
  11. H Oinonen, H Forsvik, P Ruusuvuori, O Yli-Harja, V Voipio, H Huttunen, Identity Verification Based on Vessel Matching from Fundus Images, in17th International Conference on Image Processing(Hong Kong, 2010), pp. 4089-4092.
  12. Anbarjafari, G., Face recognition using color local binary pattern from mutually independent color channels. EURASIP Journal on Image and Video Processing, 2013. 2013(1): p. 6 
  13. C-H Lin, J-L Chen, Z-L Gaing, Combining Biometric Fractal Pattern and Particle Swarm Optimization-Based Classifier for Fingerprint Recognition. Math. Probl. Eng.2010,1-15 (2010). doi:10.1155/2010/328676. 
  14. F Rossant, B Mikovicova, M Adam, M Trocan, A robust iris identification system based on wavelet packet decomposition and local comparisons ofthe extracted signatures. EURASIP J Adv Signal Process.2010, 415307 (2010). 
  15. B Nakissa, Shahram Moin M: A new user dependent iris recognition system based on an area preserving pointwise level set segmentation approach. EURASIP J Adv Signal Process.2009, 980159 (2009). doi:10.1155/2009/980159. 
  16. AK Jain, A Ross, S Prabhakar, An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4-20 (2004).  https://doi.org/10.1109/TCSVT.2003.818349
  17. RB Hill, Retinal identification(Biometrics: Personal Identification in Networked Society, Springer, Berlin, 1999), p. 126. 
  18. V VijayaKumari, N Suriyanarayanan, Blood vessel extraction using wienerfilter and morphological operation. Int J ComputSciEmerg Tech. 1(4),7-10 (2010) 
  19. Cemal Kose , Cevat Ik.ibas, A personal identification system using retinal vasculature in retinal fundus images, Expert Systems with Applications 38 (2011) 13670-13681. 
  20. Amin Dehghani, Zeinab Ghassabi,Human recognition based on retinal images and using new similarity function, Journal on Image and Video Processing, (2013). 
  21. HannuOinonen,IDENTITYVERIFICATIONBASEDONVES SELMATCHINGFROMFUNDUSIMAGES, IEEE (2010). 
  22. X. Jiang and D. Mojon, Adaptive local thresholding by verification based multithreshold probing with application to vessel detection in retinal images," IEEE Trans on Pattern Analysis and Machine Intelligence, 131-137 (2003). 
  23. Cevat Ikibas, A personal identification system using retinal vasculature in retinal fundus images, Expert Systems with Applications 38 (2011). 
  24. Sabin Tiberius Strat; Alexandre Benoit; Patrick Lambert, Retina enhanced SIFT descriptors for video indexing, Content-Based Multimedia Indexing (2013). 
  25. F. Zana and J. C. Klein, Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation, IEEE 1010-1019 (2001). 
  26. D. Selvathi; P. Lalitha Vaishnavi, Gabor wavelet based blood vessel segmentation in retinal images using kernel classifiers, Signal Processing, Communication, Computing and Networking Technologies (2011). 
  27. Michael Mayrhofer-Reinhartshuber, David J. Cornforth, Helmut Ahammer, Herbert F. Jelinek, Multiscale analysis of tortuosity in retinal images using wavelets and fractal methods, Pattern Recognition Letters, 2015. 
  28. Saleh Shahbeig, Automatic and quick blood vessels extraction algorithm in retinal images, IEEE (2013). 
  29. D. Jude Hemanth; J. Anitha, Modified cross-over techniques in Genetic Algorithm for performance enhancement of retinal image classification system, Computational Intelligence and Information Technology, (2013). 
  30. Maria Garcia, Maria I. Lopez, Daniel Alvarez, Roberto Hornero, Assessment of four neural network based classifiers to automatically detect red lesions in retinal images, Medical Engineering & Physics, (2010). 
  31. Maria Garcia, Maria I. Lopez, Daniel Alvarez, Roberto Hornero, Assessment of four neural network based classifiers to automatically detect red lesions in retinal images, Medical Engineering & Physics, (2010). 
  32. Seyed Mehdi Lajevardi, Retina Verification System Based on Biometric Graph Matching, IEEE, (2013). 
  33. Nakkiran A, Kannan K, Narendran A, Ameer A and Balaji S, RETINAL IDENTIFICATION BASED PERSONAL IDENTIFICATION SYSTEM USING SIFT ALGORITHM, 0976-1353 Volume 21 Issue 2 - APRIL 2016.