[1]
Joseph Costello
Sclera – The whites of the eye
 Iris – The colored part of the eye
 Pupil – The dark black part of the eye
 Cornea – The transparent covering of the iris and
pupil
 Retina – the lining of the inside of the eye.
 Occlusions – missing pieces of the iris blocked by
the eye lids
 Specular Reflections – reflections on the eye image
 Limbic Boundary – between the iris and sclera
 Pupillary Boundary – between the pupil and the iris

[2]
› Take an image of the eye.
 Possibly in multiple wavelengths of light.
› Isolate the iris from the other parts of the eye.
[3]
Image Acquisition
2. Segmentation
3. Normalization
1.
›
Preprocessing/Image Enhancing
Feature Extraction
5. Classification/Saving/Matching
4.

Image acquisition on mobile devices is
done from a camera, usually front facing,
on the device itself.

Depending on the camera & algorithms
used the camera could use visible light as
well as near infrared light for a more
detailed iris image.
After the image is acquired, the iris must
be separated from the rest of the image.
 These must detect the iris and its
boundary’s as well as eyelash and eyelid
occlusions.
 It also must account for iris’ at an angle,
i.e. not looking directly at the camera.

Examples of attempts to segment an iris
[4]
In order to process an iris image more
consistently, it must be in a uniform
shape.
 To get this uniform it is normalized to be a
rectangle with 0 degrees representing
the beginning of the rectangle and 360
representing the end of the rectangle.

[5]
The normalized image is usually run
through different filters and attempts are
made at removing noise
 Sometimes the purpose of the filters is to
improve the quality of the image such as
sharpness of the iris
 Other times filters are used to enhance
the unique features of the iris for better
detection.

Step 1
Step 2
Step 3
Enhancement
[6]
Next the enhanced image is processed for
feature detection and converted into an iris
code which is a machine readable
representation of the iris image.
 The generation of the iris code is simple and
there are many methods that can generate
an iris code.
 The difficulty lies in finding a method that
can generate iris codes that can reliably
maximize true positives and true negatives
while minimizing false positives and false
negatives.


What was decided?
› i.e. did the iris code match a saved iris code?
1.
Positive
2.
Negative

Was the decision correct?
›
1.
2.


i.e. did the iris code correctly match a saved iris code?
True
False
Using that criteria, a false positive means it matched a
saved iris code, but was wrong in matching it. (i.e. I now
have access to your secured data, but I am not you)
Algorithms that produce too many false positives can mean
secured data could be compromised, while too few true
positives could mean an inconvenience for the user.

A simple example for understanding iris
code generation:
› Take a normalized iris image of 64x512 pixels
resolution.
› Examine each 8x8 section of that image and
make a feature detection decision 0 or 1 based
on that small section.
› This leaves you with a 512-bit iris code.
Before an iris can be matched, it initially
needs to first be saved/trained. This involves
all the steps and the iris code (or a
representation) is saved to a database.
 Matching then compares a new iris code to
the saved code. If the algorithm determines
that the iris code matches the saved one
(within a tolerance) it returns a positive
(hopefully a true positive)

According to the NIST, current
commercially available iris recognition
systems are 90-99% accurate1.
 This is tested using a one-to-many system
where an iris is compared to a database
of many iris’ for matching.
 This is in contrast to a one-to-one
situation where an iris is compared to its
known iris code to determine accuracy

Iris recognition is much more reliable
than facial recognition for biometric
matching
 The false negative rate was about 10
times less for iris recognition vs. facial
recognition.


With the focus on biometrics in mobile
devices and the low false acceptance
rates of iris recognition, this particular
biometric is poised to be the dominant
method of authentication
Security and privacy concerns over
storing your iris on your mobile phone
have been partially mitigated by only
storing the iris code instead of the iris
image itself
 Apple’s approach to its fingerprint
biometric storage have also helped
mitigate some privacy concerns.

Instead of providing image and iris
codes directly to apps, biometric
identification is handled by a separate
hardware co-processor2.
 The stored biometric code is also
encrypted and only stored locally in the
co-processor, it is not stored in the cloud.

Since many privacy concerns stem from the
fear that your iris image will be easily
obtained by someone else or uploaded to
someone’s servers.
 I believe that once people are made
aware of how the image is not actually
stored.
 And that iris codes are protected and not
made available to anyone, they will be
much more comfortable about using iris
recognition.

Images

[1]http://i21.photobucket.com/albums/b253/qtsh820/work/irisscan1.jpg

[2]http://www.texasbiometric.com/wp-content/uploads/2012/12/eyechart1.png

[3]http://opticalengineering.spiedigitallibrary.org/article.aspx?articleid=1675825

[4]http://www.andrew.cmu.edu/user/thihoanl/iris3.jpg

[5]http://www.intechopen.com/source/html/16589/media/image53.png

[6]http://www.biometrics-system.com/images/iris_image_normalization.jpg

[7] http://www.biometricupdate.com/wp-content/uploads/2012/08/market-growthchart3.jpg

[8] http://www.cse.wustl.edu/~jain/cse571-11/ftp/biomet/fig6.jpg
Citations

1 http://fcw.com/articles/2012/04/23/nist-iris-recognition.aspx

2 https://www.apple.com/pr/library/2013/09/10Apple-Announces-iPhone-5s-The-MostForward-Thinking-Smartphone-in-the-World.html