Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/mike10004/easyeye

Iris image segmentation, encoding, and matching software
https://github.com/mike10004/easyeye

Last synced: about 2 months ago
JSON representation

Iris image segmentation, encoding, and matching software

Awesome Lists containing this project

README 

        
# easyeye



EasyEye is software for biometric iris image segmentation, encoding, 

and matching. It is in a very rough developmental state.



## Overview



EasyEye is iris image analysis software. It consists of a suite of 

command-line utilities that analyze iris images, encode iris features, 

and match iris encodings. 



The origin of the iris image analysis source code is the VASIR project

(Video-based Automatic System for Iris Recognition) from NIST. The code

has been modified, updated, reorganized and refactored to facilitate 

development.



The portions of the EasyEye source code that were developed by NIST are

in the public domain. There are many authors deserving acknowledgment 

for contributions, and they are identified in comments in the portions 

of the code that they wrote. To list a few:

- Yooyoung Lee (NIST)

- Xiaomei Liu (University of Notre Dame)

- Libor Masek (University of Western Australia)

- Peter Kovesi (University of Western Australia)



New and derived code is copyright (c) 2014 Michael Chaberski, 

distributed under the terms of the MIT License. (See LICENSE file for 

full text.)



## Does it work?

The short answer is  "I don't know, but maybe." All the pieces are there, and 

the upstream (VASIR) works, in that the segmentation and matching outputs are

rougly within range of what's expected. However, the process of deriving more 

manageable, transparent, and debuggable code from upstream -- that is, turning

VASIR into EasyEye -- could have introduced fundamental errors.



Diagnostic images produced by the software show that the core segmentation

implementation does work, in that it reliably produces reasonably accurate

results on high quality input eye images. Eyelid detection is currently 

pretty hairy, and it's uncertain whether that is a regression from the 

upstream version. Normalization, encoding, and matching have not been tested

for accuracy or reliability. In other words, it's currently possible that 

the matching results are no better than a coin flip, but we know that they

used to be better than a coin flip, and work is underway to get them back

to that performance level.



## Roadmap



The following big-picture changes are planned:



 - Support greater configurability/extensibility in image processing, 

   segmentation, and encoding (e.g. alternative boundary and eyelid 

   detection techniques, other normalization and encoding strategies)

 - Remove unnecessary legacy code (in optimasek) 

 - Add support for segmenting/encoding eye images at a distance, as 

   from frames of video (the `NIR_FACE_VIDEO` and `NIR_IRIS_VIDEO`

   data types that are supported in VASIR)

 - Provide API and executables for iris image quality analysis

 - Support Windows as a development and deployment platform



## Releases



Binaries built from the source code are available from a PPA at 

[https://launchpad.net/~mchaberski/+archive/ppa]

([https://launchpad.net/~mchaberski/+archive/ppa). Follow the PPA link for 

detailed instructions. This is the short version:



    $ sudo add-apt-repository ppa:mchaberski/ppa

    $ sudo apt-get update

    $ sudo apt-get install easyeye



This should also work on Debian, but after you add the repository, you may

want to edit the new `.list` file in `/etc/apt/sources.list.d` to align the 

Ubuntu series (e.g. saucy) with your Debian version (e.g. sid).