Improved Daugman’s Method
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Transcript of Improved Daugman’s Method
Improved Daugman’s Method
Team Beta
Bryan Chamberlain
Kyle Barkes
Daniel Lohmer
Patrick Braga-Henebry
CSE30332 Programming Paradigms
Professor Patrick Flynn
Outline
• Problem statement
• Literature Review
• Improved Daugman’s Algorithm
• Implementation Details
• Experimental Results
• Conclusions and Comments
Problem Statement
• Our team sought to create an efficient iris localization algorithm through both research and self-testing.
Literature Review• How Iris Recognition Works (John Daugman)• Image understanding for iris biometrics: A survey
(Kevin W. Bowyer, Karen Hollingsworth, Patrick J. Flynn)• An Improved Method for Daugman's Iris Localization
Algorithm (Xinying Ren, Zhiyong Peng, Qinging Zeng, Chaonan Peng, Jianhua Zhang, Shuicai Wu, Yanjun Zeng)
• Efficient Iris Recognition through Improvement of Feature Vector and Classifier (Shinyoung Lim, Kwanyong Lee, Okhwan Byeon, and Taiyun Kim)
• Iris recognition: An emerging biometric technology (Richard P. Wildes)
• Iris segmentation methodology for non-cooperative recognition (Hugo Proença, Luís A, Alexandre)
Iris segmentation methodology for non-cooperative recognition
• Extension of Wilde’s method
• Fuzzy k-means clustering
An Improved Method for Daugman's Iris Localization Algorithm
• Localization of pupillary boundary– Coarse, then fine
• Localization of limbus boundary– Coarse, then fine, based on right and left
canthus
• Localization of upper and lower eyelid boundaries– excludes area most likely to
be noise
• With pupil boundary detection:– Ability to set accurate threshold value
• With limbus boundary detection:– Noise getting in the way of edge detection on
either side of the eye
• With eyelid exclusion:– Loss of valid data– Inability to detect extreme
rotation
Concerns
Implementation• Utilized: Python, OpenCV,
Google Code, SVN
• Pupil– Coarse and Fine combined
• Limbus– Coarse
– Fine
• Main– Combines all functions
– A variety of output options
Pupilic Localization
1281282
5DCDC
=L
y
xDC
DC=L
25
• Threshold as defined in paper:• 128 proved to be a bad value• Solved for new values x and y:• With a high average gray value(DC):
• With a low average gray value(DC):
• Solved for new threshold equation:x = -8 y = 257
85
19011055190,
x=y:=L=DC
30
1206030120,
x=y:=L=DC
25782
5+DC
DC=L
Pupilic Localization Cont'd• Compare each pixel, to threshold value
• If I(x,y) < L, then it is considered to be in the pupil• Radius is determined using the
number of pixels found and the area of a circle
• Center coordinates are found by calculating the average x and y values of every pixel
πA=r /
• The integro-differential operator is then used for fine-localization
Pupilic Localization Cont'd
• f (I ) = |I (x, y) − I (x − 1, y − 1)| + |I (x, y) − I (x, y − 1)|+ |I (x, y) − I (x + 1, y − 1)|+ |I (x, y) − I (x − 1, y)|+ |I (x, y) − I (x + 1, y)|+ |I (x, y) − I (x − 1, y + 1)|+ |I (x, y) − I (x, y + 1)|+ |I (x, y) − I (x + 1, y + 1)|
• • If f(I) < , it is still in the pixel
2/If=ΔLΔL
Integro-differential operator
dr
Coarse Limbic
• Uses pupilic (x,y,r) to find limbic for L/R arcs
• Increments radius, keeps max(IDO)
• Averages L/R (x,y,r)
Fine Limbic
• Searches around coarse (x±9,y±8,r±9) for better fit
Coarse
Fine
Search Space Result
Experiments
• 51 Images, provided by Dr. P.J. Flynn
• Center: Euclidean Distance
• Radii: Absolute Difference
Experiment Data
Centers: Euclidean Distances Radii Difference
Limbic Pupillic Limbic Pupillic
Median 10.22 4.06 6.00 6.82
Mean 15.93 10.80 13.56 10.69
Std Dev
15.82 14.69 16.78 2.00
Conclusions and comments
• OO
• C++ vs. Python
• What worked well? – Integro-differential operator
– Threshold evaluator
• What didn’t work well?– Delta(intensity) check on coarse pupil