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A Study of Iris Recognition

Iris recognition is considered to be the most reliable and accurate biometric identification system available. Iris recognition system captures an image of an individual person’s eye, than the image of iris is meant for the further segmentation and normalization for extracting its feature.Segmentation is used for the localization of the correct iris region in the particular portion of an eye and it should be done accurately and correctly to remove the eyelids, eyelashes, reflection and pupil noises present in iris region. The features of the iris were encoded by convolving the normalize iris region with 1D Log-Gabor filters and phase quantizing the output in order to produce a bit-wise biometric template. The Hamming distance was chosen as a matching metric, which gave the measure of how many bits disagreed between the templates of the iris.

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International Journal of Research in advance Engineering,
Volume -1 Isuue-2, Feb-2015, Available at www.knowledgecuddle.com
21
A Study of Iris Recognition
Jaydeep Chhatbar
Information Technology department,
Gujarat Technological University,
chhatbarjaydeep123@gmail.com
Gujarat
India.
Abstract: Iris recognition is considered to be the most reliable and accurate biometric identification system
available. Iris recognition system captures an image of an individual person’s eye, than the image of iris is meant
for the further segmentation and normalization for extracting its feature.Segmentation is used for the localization
of the correct iris region in the particular portion of an eye and it should be done accurately and correctly to
remove the eyelids, eyelashes, reflection and pupil noises present in iris region. The features of the iris were
encoded by convolving the normalize iris region with 1D Log-Gabor filters and phase quantizing the output in
order to produce a bit-wise biometric template. The Hamming distance was chosen as a matching metric, which
gave the measure of how many bits disagreed between the templates of the iris.
Keywords: Iris Recognition, Image Capturing, Localization, Feature Extraction, Template Matching
I. INTRODUCTION
Iris recognition is a method of biometric recognition which use pattern recognition techniques based on high-
resolution image of iris of individual person’s. Iris scans process start with the capture of image of iris with high quality
camera. The camera closes to the subject, not more than three feet. This process takes 0 to 1 second for scanning process.
II. IRIS SYSTEM CONFIGURATION
In system configuration normally camera takes high resolution image of iris in 2D image form. Then image goes
for image processing which remove the extra part of the image else of iris. Feature extraction process converts the image
of iris in to the binary data and store in the templates. This process is for enrolment of person’s iris data in to the device.
Next process is comparison between the stored data and currently scanned data, if it matches then than device gives
permission to access the machine or device, if it does not match then it rejects the scan and asks for scan.
It
deals
with
captu
International Journal of Research in advance Engineering,
Volume -1 Isuue-2, Feb-2015, Available at www.knowledgecuddle.com
22
ring of high quality image of the iris.Concerns on the image achievement rigs, find images with enough resolution and
sharpness. Good difference in the iris pattern with proper clarification.[1]When iris is put 3 meter distance near infrared
camera, it takes the high quality image.
Iris Localization
This is the process to remove the extra part of the image. Iris can be divided in to two circles one is iris boundary
and second is pupil boundary. For iris localization we use the Dugman’s algorithm [1].
Feature Extraction
Feature encoding was implemented by convolving the normalized iris prototype with 1D Log-Gaberwavelet.This
process converts the 2D image in to the 1D image. 2D patterns are broken in to a number of 1D signals. Each row
corresponds to a circular ring on the iris region. The angular direction is taken rather than the radial one, which
corresponds to columns of normalized pattern [3].The features are extracted in codes of 0 and 1.
International Journal of Research in advance Engineering,
Volume -1 Isuue-2, Feb-2015, Available at www.knowledgecuddle.com
23
[1]
Template Matching
In template matching process, the hamming distance was chosen for recognition. The result of this computation is
then used as the goodness of match, with smaller values indicating better matches.[8]The result of hamming distance is
used as the good match. If the hamming distance are very close to 0 then matching of two patterns are better.
III. ADVANTAGES
 Very high accuracy.
 Verification time is generally less than 5 seconds.
 The eye from a dead person would deteriorate too fast to be useful, so no extra precautions have to been taken
with retinal scans to be sure the user is a living human being.[13]
IV. DISADVANTAGES
 Intrusive.
 A lot of memory for the data to be stored.
 Very expensive[13]
V. APPLICATIONS
 ATMs
 Computer login: The iris as a living password.
 Premises access control (home, office, laboratory etc.).
 Internet security, control of access to privileged information
 Automobile ignition and unlocking; anti-theft devices.
 Anti-terrorism (e.g.:— suspect Screening at airports)[7]
VI. CONCLUSION
From the above result we can conclude that iris recognition is used for high-security. Iris recognition uses
hamming distance concept and for image it uses high resolution camera. The hamming distance is close to zero, it means
the patterns are match completely.
REFERENCES
[1]. PrateekVerma, MaheedharDubey, Praveen Verma , ―Comparison of Various Segmentation Techniques in Iris Recognition‖
LAMBERT ACADEMIC PUBLISHING (LAP), GmbH & co. KG, DudweilerLandstrabe, Saarbrücken, ISBN 13: 978-3-6 59-
13597-2, Germany, MAY-2012.
[2]. Segmentation Techniques for Iris Recognition System, Iinternational Journal of Scientific & Engineering Res V volvolume 2,
Issue 4, April-2011 , ISSN 2229-5518 IJSER © @ 2011 BY Surjeet Singh, Kulbir Singh.
[3]. J. Daugman. How iris recognition works. Proceedings of 2002 International Conference on Image Processing, Vol. 1, 2002.
[4]. Chinese Academy of Sciences – Institute of Automation.Database of 756 Greyscale Eye Images. http://www.sinobiometrics.com
Version 1.0, 2003
[5]. ―Recognition of Human Iris Patterns for Biometrics Identification‖-This project is submitted for the Bachelor of Engineering
degree of the School of Computer Science and Software Engineering, The University of Western Australia, 2003 BY LIBOR
MASEK
[6]. ―An Segmentation Method for Iris Recognition System‖, Computer Vision and Intelligent Systems (CVIS) Group University
Tunku Abdul Rahman, Malaysia,2006 BY RICHARD YEW FATT NG,KAI MING MOK
[7]. ―Iris Biometric Recognition for Person Identification in Security System‖-International Journal of Computer Applications (0975
– 8887) Volume24–No.9, June2011 BY Dr.L.M.WAGHMARE, VanajaRoselin.E.Chirchi ,E.R.Chirchi
International Journal of Research in advance Engineering,
Volume -1 Isuue-2, Feb-2015, Available at www.knowledgecuddle.com
24
[8]. ―Accurate and Fast Iris Segmentation‖-International Journal of Engineering Science and Technology Vol. 2(6), 2010, 1492-1499
BY G. ANNAPOORANI, R. KRISHNAMOORTHI, P. GIFTY JEYA
[9]. Daugman, John (January 2004). "How iris recognition works" (PDF). IEEE Transactions on Circuits and Systems for Video
Technology 14 (1): 21–30. doi:10.1109/TCSVT.2003.818350. http://www.cl.cam.ac.uk/users/jgd1000/irisrecog.pdf.
[10].Daugman, John (2003). "The importance of being random: statistical principles of iris recognition" (PDF). Pattern Recognition 36
(2): 279–291. doi:10.1016/S0031-3203(02)00030-4. http://www.cl.cam.ac.uk/~jgd1000/patrec.pdf.
[11].Daugman, John (June 2005). "Results from 200 billion iris cross-comparisons".Technical Report UCAM-CL-TR-635.University
of Cambridge Computer Laboratory. http://www.cl.cam.ac.uk/TechReports/UCAM-CL-TR-635.html.
[12].Zhaofeng He, Tieniu Tan, Zhenan Sun and XianchaoQiu (15 July 2008). "Towards Accurate and Fast Iris Segmentation for Iris
Biometrics". IEEE Trans Pattern Anal Mach Intell 31 (9): 1670–84. doi:10.1109/TPAMI.2008.183. PMID 19574626.
http://www.cbsr.ia.ac.cn/users/zfhe/publications.html
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A Study of Iris Recognition

  • 1. International Journal of Research in advance Engineering, Volume -1 Isuue-2, Feb-2015, Available at www.knowledgecuddle.com 21 A Study of Iris Recognition Jaydeep Chhatbar Information Technology department, Gujarat Technological University, chhatbarjaydeep123@gmail.com Gujarat India. Abstract: Iris recognition is considered to be the most reliable and accurate biometric identification system available. Iris recognition system captures an image of an individual person’s eye, than the image of iris is meant for the further segmentation and normalization for extracting its feature.Segmentation is used for the localization of the correct iris region in the particular portion of an eye and it should be done accurately and correctly to remove the eyelids, eyelashes, reflection and pupil noises present in iris region. The features of the iris were encoded by convolving the normalize iris region with 1D Log-Gabor filters and phase quantizing the output in order to produce a bit-wise biometric template. The Hamming distance was chosen as a matching metric, which gave the measure of how many bits disagreed between the templates of the iris. Keywords: Iris Recognition, Image Capturing, Localization, Feature Extraction, Template Matching I. INTRODUCTION Iris recognition is a method of biometric recognition which use pattern recognition techniques based on high- resolution image of iris of individual person’s. Iris scans process start with the capture of image of iris with high quality camera. The camera closes to the subject, not more than three feet. This process takes 0 to 1 second for scanning process. II. IRIS SYSTEM CONFIGURATION In system configuration normally camera takes high resolution image of iris in 2D image form. Then image goes for image processing which remove the extra part of the image else of iris. Feature extraction process converts the image of iris in to the binary data and store in the templates. This process is for enrolment of person’s iris data in to the device. Next process is comparison between the stored data and currently scanned data, if it matches then than device gives permission to access the machine or device, if it does not match then it rejects the scan and asks for scan. It deals with captu
  • 2. International Journal of Research in advance Engineering, Volume -1 Isuue-2, Feb-2015, Available at www.knowledgecuddle.com 22 ring of high quality image of the iris.Concerns on the image achievement rigs, find images with enough resolution and sharpness. Good difference in the iris pattern with proper clarification.[1]When iris is put 3 meter distance near infrared camera, it takes the high quality image. Iris Localization This is the process to remove the extra part of the image. Iris can be divided in to two circles one is iris boundary and second is pupil boundary. For iris localization we use the Dugman’s algorithm [1]. Feature Extraction Feature encoding was implemented by convolving the normalized iris prototype with 1D Log-Gaberwavelet.This process converts the 2D image in to the 1D image. 2D patterns are broken in to a number of 1D signals. Each row corresponds to a circular ring on the iris region. The angular direction is taken rather than the radial one, which corresponds to columns of normalized pattern [3].The features are extracted in codes of 0 and 1.
  • 3. International Journal of Research in advance Engineering, Volume -1 Isuue-2, Feb-2015, Available at www.knowledgecuddle.com 23 [1] Template Matching In template matching process, the hamming distance was chosen for recognition. The result of this computation is then used as the goodness of match, with smaller values indicating better matches.[8]The result of hamming distance is used as the good match. If the hamming distance are very close to 0 then matching of two patterns are better. III. ADVANTAGES  Very high accuracy.  Verification time is generally less than 5 seconds.  The eye from a dead person would deteriorate too fast to be useful, so no extra precautions have to been taken with retinal scans to be sure the user is a living human being.[13] IV. DISADVANTAGES  Intrusive.  A lot of memory for the data to be stored.  Very expensive[13] V. APPLICATIONS  ATMs  Computer login: The iris as a living password.  Premises access control (home, office, laboratory etc.).  Internet security, control of access to privileged information  Automobile ignition and unlocking; anti-theft devices.  Anti-terrorism (e.g.:— suspect Screening at airports)[7] VI. CONCLUSION From the above result we can conclude that iris recognition is used for high-security. Iris recognition uses hamming distance concept and for image it uses high resolution camera. The hamming distance is close to zero, it means the patterns are match completely. REFERENCES [1]. PrateekVerma, MaheedharDubey, Praveen Verma , ―Comparison of Various Segmentation Techniques in Iris Recognition‖ LAMBERT ACADEMIC PUBLISHING (LAP), GmbH & co. KG, DudweilerLandstrabe, Saarbrücken, ISBN 13: 978-3-6 59- 13597-2, Germany, MAY-2012. [2]. Segmentation Techniques for Iris Recognition System, Iinternational Journal of Scientific & Engineering Res V volvolume 2, Issue 4, April-2011 , ISSN 2229-5518 IJSER © @ 2011 BY Surjeet Singh, Kulbir Singh. [3]. J. Daugman. How iris recognition works. Proceedings of 2002 International Conference on Image Processing, Vol. 1, 2002. [4]. Chinese Academy of Sciences – Institute of Automation.Database of 756 Greyscale Eye Images. http://www.sinobiometrics.com Version 1.0, 2003 [5]. ―Recognition of Human Iris Patterns for Biometrics Identification‖-This project is submitted for the Bachelor of Engineering degree of the School of Computer Science and Software Engineering, The University of Western Australia, 2003 BY LIBOR MASEK [6]. ―An Segmentation Method for Iris Recognition System‖, Computer Vision and Intelligent Systems (CVIS) Group University Tunku Abdul Rahman, Malaysia,2006 BY RICHARD YEW FATT NG,KAI MING MOK [7]. ―Iris Biometric Recognition for Person Identification in Security System‖-International Journal of Computer Applications (0975 – 8887) Volume24–No.9, June2011 BY Dr.L.M.WAGHMARE, VanajaRoselin.E.Chirchi ,E.R.Chirchi
  • 4. International Journal of Research in advance Engineering, Volume -1 Isuue-2, Feb-2015, Available at www.knowledgecuddle.com 24 [8]. ―Accurate and Fast Iris Segmentation‖-International Journal of Engineering Science and Technology Vol. 2(6), 2010, 1492-1499 BY G. ANNAPOORANI, R. KRISHNAMOORTHI, P. GIFTY JEYA [9]. Daugman, John (January 2004). "How iris recognition works" (PDF). IEEE Transactions on Circuits and Systems for Video Technology 14 (1): 21–30. doi:10.1109/TCSVT.2003.818350. http://www.cl.cam.ac.uk/users/jgd1000/irisrecog.pdf. [10].Daugman, John (2003). "The importance of being random: statistical principles of iris recognition" (PDF). Pattern Recognition 36 (2): 279–291. doi:10.1016/S0031-3203(02)00030-4. http://www.cl.cam.ac.uk/~jgd1000/patrec.pdf. [11].Daugman, John (June 2005). "Results from 200 billion iris cross-comparisons".Technical Report UCAM-CL-TR-635.University of Cambridge Computer Laboratory. http://www.cl.cam.ac.uk/TechReports/UCAM-CL-TR-635.html. [12].Zhaofeng He, Tieniu Tan, Zhenan Sun and XianchaoQiu (15 July 2008). "Towards Accurate and Fast Iris Segmentation for Iris Biometrics". IEEE Trans Pattern Anal Mach Intell 31 (9): 1670–84. doi:10.1109/TPAMI.2008.183. PMID 19574626. http://www.cbsr.ia.ac.cn/users/zfhe/publications.html