Project sunbeam
This presentation is the property of its rightful owner.
Sponsored Links
1 / 11

Project Sunbeam PowerPoint PPT Presentation


  • 75 Views
  • Uploaded on
  • Presentation posted in: General

Project Sunbeam. Limbic Detection. Normalized Original. Segmentation Result. Mobile Iris Recognition. Pupil Detection. Unwrapped . Project Goals • Pupil Detection • Going Forward • More Information . Project Goals and Timeline.

Download Presentation

Project Sunbeam

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Project sunbeam

Project Sunbeam

Limbic Detection

Normalized Original

Segmentation Result

Mobile Iris Recognition

Pupil Detection

Unwrapped


Project goals and timeline

Project Goals • Pupil Detection • Going Forward • More Information

Project Goals and Timeline

Problem Statement: To-date there is no mainstream mobile platform for Iris Biometrics that does not require either dedicated hardware or tethering to a computer.

As of March 2012¹: 50.4% of U.S. adults own a smartphone. 24.4% Android. 16.1% iOS.

Project Sunbeam aims to create “One-to-Many” Iris Recognition Platform for Mobile Devices such as Apple iOS and Google Android operating systems.

Platform Segmented into Two Main Components:Library (Framework)Focus on the algorithms designed to segment and produce identifiable iris signatures (Iris Bitcode).

Mobile Application (Front-End)Focus on Mobile Application Implementation and Mobile Platform Limitations. Mobile Application implements Framework.

¹Neilsen Ratings: http://blog.nielsen.com/nielsenwire/?p=31688


Project goals and timeline1

Project Goals • Pupil Detection • Going Forward • More Information

Project Goals and Timeline

Project’s current focus is on finishing the framework to a complete working state.

Recently finished Entire Process to Produce Iris Signature (Iris Bitcode) from Normalized Image. Framework approx. 95% functional.

Segmented Iris (Cartesian Coordinates)

Segmented Iris(Polar Coordinates)

2048-Byte Iris Bitcode(1/8 of the Entire Bitcode)

Normalized Image

Segmented Image


Pupil detection

Project Goals • Pupil Detection • Going Forward • More Information

Pupil Detection

One of the first tasks for the Framework was to segment the Pupil from the Iris.

How?Because the pupil has the lowest intensity (darkest color) we expect that we can look at the entire image and determine which pixels are apart of the pupil.

First we look at each pixel in the image and find the average intensity over the entire image. We then calculate an intensity threshold.


Pupil detection1

Project Goals •Pupil Detection • Going Forward • More Information

Pupil Detection

How?Using the Intensity Thresholdwe now check every pixel in the image against the threshold. If a single pixel’s intensity is below the threshold, we conclude that it is part of the pupil.


Pupil detection2

Project Goals •Pupil Detection • Going Forward • More Information

Pupil Detection

Problem?Because eye lashes, makeup, and other portions of the face can also have a low intensity, they can be mistaken as part of the pupil, misaligning the pupil location.

SolutionWe found through empirical testing that the initial guess, although misaligned, is within close proximity of the actual pupil. We now look at only the pixels within a square of 25% width and height around the initial guess.

This now allows us to accurately detect the pupil’s location and size.


Pupil detection3

Project Goals •Pupil Detection • Going Forward • More Information

Pupil Detection


Pupil detection4

Project Goals •Pupil Detection • Going Forward • More Information

Pupil Detection


Going forward

Project Goals •Pupil Detection • Going Forward • More Information

Going Forward

Short TermComplete Framework to a 100% working solution.(This Semester)Involve more test cases and adjust/improve algorithms as necessary.Empirical Iris Bitcode matching testing and statistics.

Long TermStart of Mobile Application.(Next Semester)Iris characterization and matching optimization.

Final ResultFully-working proof of concept Mobile Iris Recognition platform.


More information

Project Goals •Pupil Detection • Going Forward • More Information

More Information

What we covered…

The need for a Mobile Iris Recognition Platform.

No current solution for iOS or Android devices.

No current mobile solution that does not require additional hardware.

Overview of the Pupil Segmentation Process.

Determining the boundaries and size of the pupil.


More information1

Project Goals •Pupil Detection • Going Forward • More Information

More Information

For more information: http://sunbeam.tech.mtu.edu.

Project website will be periodically updated with progress updates, newer images, and code segments.

Matthew [email protected] [email protected]

Advisor: Professor Hembroff


  • Login