Study Of Iris Recognition
Schemes
By:Ritika Jain
ritika.jain@mavs.uta.edu
Under guidance of
DR K R RAO
UNIVERSITY OF TEXAS AT
ARLINGTON
SPRING 2012
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.
AN INTRODUCTION[19]
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.
A GOOD BIOMETRIC[19]
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.
IRIS RECOGNITION[19]
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.
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.
• 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.
IRIS
Figure 1: The human eye with
the location of iris. [19]
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.
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.
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).
• most biometric systems allow two modes of operation : an
enrolment mode and an identification mode.
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.
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.
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.
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.
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.
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.
• [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.
• [10] P. Kovesi "MATLAB functions for computer vision and
image analysis", available at:
http://www.cs.uwa.edu.au/~pk/Research/MatlabFns/index.ht
ml.
• [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 at:
Advancedsourcode.com:
http://www.advancedsourcecode.com/iris.asp
• [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.
• [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,
Wikipedia:http://www.wikipedia.org/.
• [20] K.R.Rao and P.Yip, ”Discrete cosine transform”, Boca
Raton, FL: Academic press, 1990.
THANKYOU