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Study of Iris Pattern Matching and Detection of the Persons having Squint

Affiliations

  • Department of CSE, National Institute of Technology Agartala, Agartala - 799055, Tripura, India
  • Department of EIE, National Institute of Technology Agartala, Agartala - 799055, Tripura, India

Abstract


Objectives: The present paper has attempted a quantitative study to measure the amount of off centricity of the iris from its normal position. Methods/Statistical Analysis: The eye image having squint has been taken from real life image (CASIA DATABASE). First of all the central position of the eye opening between the eyelids are detected by Matlab code. Then the distance between the locations of the squint eye are taken by using 3X3 spatial filter with a local searching method. Findings: Squint is the off centre location of iris and pupils and may occur either in one eye or both of the eyes and both male and female persons may have squint. The local searching method terminates when the centre of the filter coincides with the centre of the pupil of the squint eye. The filters are moved from the centre of the eye by coinciding the centre of the spatial filter with the centre of the eye in vertical, horizontal and angular directions. Application/Improvement: By this process noises will be reduced which are prevalent in squint eye. The number of pixels responsible for the exact locations may be compensated by making the successive position of the filters bit overlapping which will in turn reduce the possibility of having checker board appearance in the output of the image.

Keywords

Detection, Iris, Pattern Matching, Squint.

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References


  • Daugman J. How Iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology. Jan 2004; 14(1):21–30.
  • Bodunde OT, Onabolu OO, Fakolujo VO. Pattern of squint presentations in children in a tertiary institution in Western Nigeria. IOSR Journal of Dental and Medical Sciences (IOSR-JDMS). May 2014; 13(5):29–31.
  • Wildes R, Asmuth J, Green G, Hsu S, Kolczynski R, Matey J, McBride S. A system for automated iris recognition.Proceedings IEEE Workshop on Applications of Computer Vision; Sarasota, FL. 1994. p. 121–8.
  • Masek L. Recognition of human iris patterns for biometric identification. School of Computer Science and Soft Engineering, the University of Western Australia; 2003.
  • Gonzalez RC, Woods RE. Digital Image Processing. 2nd ed.New Jersey: Prentice Hall.
  • Jain AK, Ross A, Prabhaker S. An introduction to biometric recognition. IEEE Transactions Circuits and Systems for Video Technology. 2004; 14(1):4–20.
  • Daugman J. New methods in iris recognition. IEEE Trans System, Man, and Cybernetics-Part B: Cybernetics. 2007; 37(5):1167–75.
  • Wildes R. Iris recognition, an emerging biometric technology.Proceedings of the IEEE. 1997; 85(9):1348–63.
  • Ma L, Tan T, Wang Y, Zhang D. Personal identification based on iris texture analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2003 Dec; 25(12):1519–33.
  • Wood ONM, Higgins PT. Biometrics identification. Berkeley, California: The McGraw-Hill Company; 2002.
  • Mira J, Mayer J. Image feature extraction for application of biometric identification of iris: A morphological approach.IEEE Proceedings 16 Brazilian Symposium on Computer Graphics and Image Processing; 2003. p. 391–8.
  • Biometric personal identification based on iris patterns.Available from: http://visgraph.cs.ust.hk/biometrics/ Papers/Iris/cpr2000-02-01.pdf
  • Canny J. A computational approach to edge detection. IEEE Transaction on Pattern Analysis and Machine Intelligence.1986; 8:679–714.

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