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Study Of Iris Recognition Schemes

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Study Of Iris Recognition Schemes By:Ritika Jain ritika.jain_at_mavs.uta.edu Under guidance of DR K R RAO UNIVERSITY OF TEXAS AT ARLINGTON SPRING 2012 – PowerPoint PPT presentation

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Title: Study Of Iris Recognition Schemes


1
Study Of Iris Recognition Schemes
  • ByRitika Jain
  • ritika.jain_at_mavs.uta.edu
  • Under guidance of
  • DR K R RAOUNIVERSITY OF TEXAS AT ARLINGTON
  • SPRING 2012

2
PROPOSAL
  • This project is focussed upon studying and
    implementing the various iris recognition schemes
    available and an analysis of the different
    algorithms using Chinese academy of sciences
    institute of automation (CASIA) 14 database.

3
AN INTRODUCTION19
  • Biometric technology is widely used for personnel
    identity identification. 
  • A biometric system provides automatic
    identification of an individual.
  •  Typical biometric technologies include
    fingerprint identification, face recognition,iris
    recognition etc. Iris  recognition  is regarded
    as the most reliable and accurate biometric
    identification system available.

4
A GOOD BIOMETRIC19
  • A good biometric is characterized by use of a
    feature that is
  • highly unique so that the chance of any two
    people having the same characteristic will be
    minimal
  • stable  so that the feature does not change over
    time
  • can be easily captured in order to provide
    convenience to the user
  • prevent misrepresentation of the feature.
  •  

5
IRIS RECOGNITION19
  • Iris recognition is amongst the most robust and
    accurate biometric technique available in the
    market today with existing large scale
    applications supporting databases in excess of
    millions of people. 
  • The iris is a protected organ whose random
    texture is stable throughout the life and hence
    can be used as an identity document offering a
    very high degree of identity assurance.

6
ADVANTAGES OF USING IRIS AS A RECOGNITION SCHEME
19
  • Iris is an internal organ that is well protected
    against damage and wear by a highly transparent
    and sensitive membrane (the cornea). This
    distinguishes it from fingerprints, which can be
    difficult to recognize after years of certain
    types of manual labor.
  • The iris is mostly flat, and its geometric
    configuration is only controlled by two
    complementary muscles (the sphincter pupillae and
    dilator pupillae) that control the diameter of
    the pupil. This makes the iris shape far more
    predictable than, for instance, that of the face.

7
 
  • There is no need for the person being identified
    to touch any equipment that has recently been
    touched by a stranger, thereby eliminating an
    objection that has been raised in some cultures
    against fingerprint scanners, where a finger has
    to touch a surface, or retinal scanning, where
    the eye must be brought very close to an eyepiece
    (like looking into a microscope)
  • An iris scan is similar to taking a photograph
    and can be performed from about 10 cm to a few
    meters away.  
  •        Figure 1 shows the location of iris. 

8
IRIS
  •  

Figure 1 The human eye with the location of
iris. 19 
9
DISADVANTAGES OF USING IRIS FOR IDENTIFICATION
19
  • As with other photographic biometric
    technologies, iris recognition is susceptible to
    poor image quality, with associated failure to
    enroll rates. 
  • Many commercial Iris scanners can be easily
    fooled by a high quality image of an iris or face
    in place of the real thing.
  • The accuracy of scanners can be affected by
    changes in lighting 
  • As with other photographic biometric
    technologies, iris recognition is susceptible to
    poor image quality, with associated failure to
    enroll rates. 

10
OVERVIEW
  • In the project the various algorithms are
    discussed and analyzed like as proposed by
    Daugman 2, 5, 6
  • Recognition of human iris patterns for biometric
    identification as proposed by Masek 10, 11
  • Phase based iris identification by Miyawaza 15,
  • discrete cosine transform (DCT) 20 based Iris
    recognition by Monro et al 16 and other like
    techniques available.
  • The various functions, filters and the processes
    involved will be studied and the results are
    compared on the basis of previous studies of that
    approach.CASIA iris image database 14 will be
    used for images of iris to be analyzed for the
    different codes.

11
SUMMARY OF WORKING OF BIOMETRIC SYSTEMS 3
  • Biometric systems work by
  • first capturing a sample of the feature
  • sample is then transformed using some sort of
    mathematical function into a biometric template
  • the biometric template will provide a normalized,
    efficient and highly discriminating
    representation of the feature (which can then be
    objectively compared with other templates in
    order to determine identity). 

12
 
  • most biometric systems allow two modes of
    operation an enrolment mode and an
    identification mode.

13
LIBOR MASEK'S PRINCIPLE 3
  • The iris recognition system consists of
  • automatic segmentation system that is based on
    the Hough transform 3 and is able to localize
    the circular iris and pupil region
  • The extracted iris region is then normalized into
    a rectangular block with constant dimensions to
    account for imaging inconsistencies.
  • Finally, the phase data from 1D Log-Gabor filters
    3 was extracted and quantized to four levels to
    encode the unique pattern of the iris into a
    bit-wise biometric template.
  •  

14
 
  • The Hamming distance 3 was employed for
    classification of iris templates, and
    two templates were found to match if a test of
    statistical independence was failed. 

15
 
  • The system is  composed of a number of
    sub-systems 3, which correspond to each stage
    of iris recognition. 
  • These stages are as follows
  • segmentation locating the iris region in an eye
    image 
  • normalization creating a dimensionally
    consistent representation of the iris region
  •  feature encoding creating a template
    containing only the most discriminating features
    of the iris
  • The input to the system is an eye image, and the
    output is be an iris template, which will provide
    a mathematical representation of the iris region.

16
 
  • By understanding the techniques available and
    doing a comparative study of their advantages,
    disadvantages and efficiency, an analysis will be
    made about the features involved, the advantages
    and the shortcomings and the results will be
    compared with the previous studies conducted
    using the different methods.The project will be
    extended to modify the code to include a more
    vast database with increased genuine detection.

17
SCOPE AND FUTURE EXTENSION
  • The project can be extended to include a vast
    database with increased genuine detection which
    involves forming more templates and improvising
    the current code. The hardware of the equipment
    can be worked upon and  improvised which is used
    to capture the image to improve the performance.  

18
REFERENCES
  • 1 J. Daugman, "High confidence visual
    recognition of persons by a test of statistical
    independence", IEEE Transactions on Pattern
    Analysis and Machine Intelligence, Vol.15, No.11,
    pp.1148-1160, November, 1993.
  • 2  J. Daugman, " How iris recognition works",
    IEEE Transactions on circuits and systems for
    video technology, Vol.14, No.1, pp.21-30,
    January, 2004.
  • 3 L. Masek, "Recognition of human iris patterns
    for biometric identification", M.S. thesis,
    University of Western Australia, 2003.
  • 4 R. Wildes, " Iris recognition an emerging
    biometric technology", Proceedings of the IEEE,
    Vol. 85, No. 9, pp.1348-1363, September, 1997.
  • 5 J. Daugman, Biometric personal identification
    system based on iris analysis. United States
    Patent, Patent Number 5,291,560,1994.

19
 
  • 6  S. Sanderson and J. Erbetta, "
    Authentication for secure environments based on
    iris scanning technology", IEE Colloquium on
    Visual Biometrics, pp.8/1-8/7, March, 2000.
  • 7   R. Wildes, J. Asmuth, G. Green, S. Hsu, R.
    Kolczynski, J. Matey and S. McBride, " A system
    for automated iris recognition", Proceedings IEEE
    Workshop on Applications of Computer Vision,
    Sarasota, FL, pp.121-128, December, 1994.
  • 8  W. Boles and B. Boashash, " A human
    identification technique using images of the iris
    and wavelet transform", IEEE Transactions on
    Signal Processing, Vol. 46, No. 4, pp.185-1188,
    April, 1998.
  • 9 A. Gongazaga and R.M. da Costa, " Extraction
    and selection of dynamic features of human iris",
    IEEE Computer Graphics and Image Processing, Vol.
    XXII, pp.202-208, October, 2009.

20
  • 10 P. Kovesi "MATLAB functions for computer
    vision and image analysis", available at
    http//www.cs.uwa.edu.au/pk/Research/MatlabFns/in
    dex.html.
  • 11 L. Masek and P. Kovesi, MATLAB source code
    for a biometric identification system based on
    iris patterns, The school of computer science
    and software engineering, The university of
    western Australia, 2003.
  • 12 D.M. Monro, S.Rakshit and Z. Dexin, "DCT
    based iris recognition, IEEE Transactions on
    pattern analysis and machine intelligence, Vol.
    29, Issue 4, pp.586-595, April, 2007.
  • 13 Different sample source codes available
    atAdvancedsourcode.com http//www.advancedsourc
    ecode.com/iris.asp

21
 
  • 14 Chinese academy of sciences - institute of
    automation, database of greyscale eye
    images http//www.cbsr.ia.ac.cn/IrisDatabase.htm
  • 15 K. Miyazawa, K. Ito, K. Aoki, T. Kobayashi
    and K. Nakajima, " An efficient iris recognition
    algorithm using phase based image matching ",
    IEEE International conference on image
    processing, pp.325-328, September, 1995.   
  • 16 W. Kong and D. Zhang," Accurate iris
    segmentation based on novel reflection
    and eyelash detection model", Proceedings of 2001
    International Symposium on Intelligent
    Multimedia, Video and Speech Processing, Hong
    Kong, pp.263-266, May, 2001.

22
 
  • 17 N. Ritter, "Location of the pupil-iris
    border in slit-lamp images of the cornea",
    Proceedings of the International Conference on
    Image Analysis and Processing, pp.740-745,
    September, 1999. 
  • 18 Y. Zhu, T. Tan and Y. Wang, Biometric
    personal identification based on iris patterns
    ,Proceedings of the 15th International Conference
    on Pattern Recognition, Spain, Vol. 2,
    pp.801-804, February, 2000. 
  • 19 Online free encyclopedia, Wikipediahttp//ww
    w.wikipedia.org/.
  • 20 K.R.Rao and P.Yip, Discrete cosine
    transform, Boca Raton, FL Academic press, 1990.

23
THANKYOU
  •  
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