This document describes iris and periocular recognition techniques. It discusses segmentation, normalization, feature extraction and matching steps for iris recognition. Segmentation involves localization of the iris and eyelid detection. Normalization maps the iris to polar coordinates. Features are represented as a 2048-bit iris code. Periocular recognition uses the area around the eye for identification. The document tests the techniques on three datasets, achieving 100% accuracy even with noise, blur and transformations added to query images. Processing time increases with the number of keypoints and image size.

1. IRIS RECOGNITION
Shashank Dhariwal
Aisha Jabeen

2. Scheme
Iris Recognition
Fig: 1-Iris Region
2
Periocular
Recognition
Fig: 2-Periocular Region

3. Final Detection
Iris Recognition Output Image
=0
Images Matched
Sum of Difference
Periocular
Recognition Output Image
3
>0
Images not matched

4. Iris Recognition
 Segmentation
 Normalization
Fig: 3-Segmented Image [1]
 Feature Extraction
 Template Matching
Fig: 4-Normalized Image [1]
4

5. Segmentation
 Localization
 Circular Hough Transform
 Canny edge detection to
generate edge map
 Gradients biased in
vertical direction for outer
iris/sclera boundary
 Vertical and horizontal
gradients weighted equally
for inner iris/ pupil
boundary
 6 parameters stored at the
end
5
Fig: 5-Localised Image

6. Segmentation
 Eyelid/Eyelash
detection
 Linear Hough Transform




used for fitting line
2nd horizontal line drawn
Canny edge detection for
edge map
Only horizontal gradient
information taken
Simple thresholding for
isolating eyelashes
Fig: 6-Eyelid/Eyelash occlusion
6

7. Normalization
 Counters dimensional
inconsistencies
 Produces iris regions with
constant dimensions
 Remaps each point within
iris region to a pair of polar
coordinates
Fig : 7- Daugman’s rubber sheet model [2]
7

8. Normalization
 Radial & Angular resolution
 Pupil being non-concentric
 Normalized pattern created
by backtracking Cartesian
data points.
 2D arrays for polar
coordinates, and marking
reflections, eyelashes and
eyelids
 Data points occurring along
pupil border are discarded
8
Fig : 8- Result of Normalization

9. Feature Extraction
 Represent iris texture
as a binary vector of
2046 bit
Iris Code
Textured
region
9
Fig: 9-Iris code & Textured region [2]

10. 8 Bands * 128 Textures
10
Fig: 10-Textures [2]

11. Feature Encoding

11

12. Feature Encoding
12

13. Matching
 Bit-wise comparison
 Measured by
Hamming distance
 K-nearest neighbour
classification
13

14. Periocular Biometrics

15. Definition
The process of identifying a person based on the study of area around
the eye, namely the edges of eye, eyebrows, eye lashes and skin.[3]
It is the region of interest that defines the method to be used for feature
extraction and
matching, and are broadly classified as Global Matcher (uses
information about colour, texture and shape) or Local Matcher (uses the information
contained in Key Points).
Fig 1. Area of interest [3]
15
Fig 2. Figure showing key points
obtained using SIFT [3]

16. Why Periocular
 Iris -
Iris is a moving object located in another moving object eye ball which is
again located in moving head which again is connected to a moving body. (lot
of movements!!!!)
- Small surface area (difficult to capture!!!!)
- Typically imaged in NIR (appropriate lighting required to illuminate!!!!)
- Requires subject co operation (as if thugs would co operate!!!!)
- Occlusion by eyelids and flowing hair affected the results.
 Retina
– Typically a coherent light source required to illuminate
- Again Subject Co operation is required.
 Face
good
- There is a trade off between the recognition based on Iris and Face.
- Iris requires the subject to be close to camera, so we miss out the facial info
- Face requires subject to be at some distance from the camera, we can’t get
resolution image for Iris.
Periocular – could use colour and NIR both, distance to camera not a problem and
best no subject co operation required.
16

17. Periocular Success
 Periocular –
introduced by Park in year 2009 [3],
used colour images obtained from an off the shelf Cannon camera.
Accuracy – 77% (combined- Local + Global) 958 images
- Later in year 2010, Woodard [4] combined Iris with Periocular
used 520 NIR images database
Local Binary Pattern as the global matching method
Accuracy - combined - 96.5% , Iris – 13.8% and Periocular – 92.5%
.
- Present project
NIR images used (Three database of varying size 40 - 77)
SIFT [5] as Local matching technique.
Accuracy – 100% !!!!!!
17

18. Extraction and Matching
 Methods
Global Feature extraction and matching
matching
Local Feature extraction and
Fig 4. Local Descriptor
Construction [3]
Fig 3 . Global Descriptor
Construction [3]
-
18
Gradient Based (GO) histogram.
Local Binary Pattern (LBP)
- SIFT
- SURF
Euclidean distance used to calculate matching
-
Distance ratio based matching
Squared Euclidean distance

19. Implementation
 Dataset used
 DB1 – 40 NIR images from CASIA V3_2– Iris – Twins
 DB2 – 36 NIR images from CASIA V3_2– Iris – lamp
 DB3 – 77 NIR images from CASIA V3_2– Iris – Interval
 Parameters for SIFT
 Detection (Key point - centre coordinates, size/scale, orientation/theta )

Octaves
–
dynamically set as per the size of the image – log2(min (width, length))
(inversely proportional to image resolution)

Scale
-
3
(smoothing level)

Peak Threshold
-
0
(high value will eliminate key points)

Edge Threshold
-
10
( low value will eliminate more key points)
 Description

Magnification factor
- 3

Gaussian Window Size
- 1.5 x scale of key point (smaller values let the centre of descriptor count more)
(descriptor size is determined by multiplying the key point scale by this factor.)
 Matching
– 1.5

19
Threshold

Measured by L2 norm for min difference between two descriptors
(Squared Euclidean Distance Ratio)

20. Implementation
 System Details
 Processor – Intel (R) Xenon (R) 2.67GHz, 64-bit
 Ram
- 12.0 GB
 Matlab ver - R2011a
 Various Tests
 Test 1
 Test 2
 Test 3
 Test 4
 Test 5
 Test 6
20
– Query image from same data set
– Query image from other data set
– Effect on identification when Noise added to query image
– Effect on identification when Blur added to query image
– Effect on identification when query image is rotated
– Effect on identification when query image is scaled

21. Results
Test 1 (a) – Database – DB1 Query image – DB1
40 query images
Accuracy – 100%
1600
Matches Found
Time Taken
6
1400
No of Matches Found
1200
4
1000
800
3
600
2
400
1
200
21
0
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
Query Image No
Time taken to find the match(s)
5

22. Results
Test 1 (b) – Database – DB2 Query image – DB2
36 query images
Accuracy – 100%
Matches Found
Time Taken
1600
6
5
No of Matches found
1200
4
1000
800
3
600
2
400
1
200
0
22
0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Query Image No
Time Taken to find the match(s)
1400

23. Results
Test 1 (c) – Database – DB3 Query image – DB3
77 query images
Accuracy – 100%
Matches found
Time taken
1.4
450
1.2
No of Matches Found
400
1
350
300
0.8
250
0.6
200
150
0.4
100
0.2
50
0
0
0 1 2 3 4 5 6 7 8 910 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76
11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77
23
Query Image No
Time taken to find the matches(s)
500

24. Results
Test 2 (a ) –
Database – DB1 Query image – DB2
36 query images
Accuracy – 100%
Test 2 (b ) –
Database – DB2 Query image – DB1
40 query images
Accuracy – 100%
24

25. Results
Test 3 (a) – Salt & Pepper Noise
Query Image
25
20% Salt & pepper
Noise
Enter the query image = 27
No of Matches = 25
Match Found , Image 27
Elapsed time is 1.404492
seconds.
Matched Image

26. Results
Test 3 (b) – Gaussian Noise
Query Image
26
Mean = 0 ; variance =
.05
Enter the query image = 10
No of Matches = 30
Match Found , Image 10
Elapsed time is 1.357519
seconds.
Matched Image

27. Results
Test 4 – Blurring
Query Image
27
Linear = 20 ; Theta =
35
Enter the query image = 15
No of Matches = 137
Match Found , Image 15
Elapsed time is 3.862147
seconds.
Matched Image

28. Results
Test 5 – Rotation
Query Image
28
Deg = 80
Enter the query image = 20
No of Matches = 1034
Match Found , Image 20
Elapsed time is 4.084018
seconds..
Matched Image

29. Results
Test 6 – Scaling
Query Image
29
Ratio = .5
Enter the query image = 5
No of Matches = 127
Match Found , Image 5
Elapsed time is 0.646144
seconds.
Matched Image

30. Analysis
 With the given size of the database this method
has given an accuracy of 100% even when
introduced by noise, blur and transformation.
(however it is prudent to test this method on a larger database)
 Time taken to match is found to be proportional
to the no of key points selected and hence the
number of matches.
 Time taken is also proportional to the size of the
image, for e.g in Test 1 (c) the size of the image is
280 x 320 against the image size 480 x 640 in
Test 1 (a & b) ,the time taken for match is quarter
of that taken in Test 1 (a & b).
30

31. Reference
[1] Libor Masek, ”Recognition of Human Iris Patterns for Biometric Identification”
Bachelors Thesis, The University of Western Australia, 2003
[2] J. Daugman (2004). “How iris recognition works”, IEEE Trans. CSVT, vol. 14, no.
1, pp. 21 – 30.
[3] U. Park, A. Ross, and A.K. Jain, “Periocular biometrics in the visible spectrum: a
feasibility study”, in Proceedings of the 3rd IEEE International Conference on
Biometrics: Theory, Applications and systems, 2009, pp. 153–158.
[4] D. Woodard, S Pundlik, P Miller, “ On the Fusion of Periocular and Iris Biometrics
in Non – Ideal Imagery”, in International conference on Pattern recognition, 2010.
[5] A Vedaldi and B Fulkerson http://ww w.vlfeat.org
31

32. DEMO
32