Thank you for your interest in the irissometry toolbox!

The irissometry implementation does the following:

• Detects the eye's pupil in close-up videos using a starburst-like algorithm (see function detectPupil())
• Tracks points and calculates distances within the iris (see function irissometry())
• Outputs a matrix containing data about the pupil center coordinates, pupil size, iris feature distances, etc.
• Saves the output matrix in .mat file and .csv file
• Plots some graphs with pupil radius and position data over time (see plotResults.m)

See "example.m" for an example code. Just run it, select one or more videos, and observe the magic.

For more info about input and output (io), enter "help irissometry" in the command.

Please cite our work in case you use our implementation!

Strauch, C., & Naber, M. (Submitted). Irissometry: effects of pupil size on iris elasticity measured with video-based feature tracking. Investigative Ophthalmology and Vision Sciences.

Make sure that the videos do not display black borders at the edge of the frame because this causes the pupil border detection to fail. This is how a video frame should look like:

The code has been tested in MATLAB 2019b on a windows 10 machine. No guarantees can be provided for other MATLAB versions and operating platforms.

Please note that you need the following toolboxes to get the code to work:

• Computer vision toolbox
• Image processing toolbox
• Signal processing toolbox
• Statistics and machine learning toolbox

If you get an error about a missing function, it is likely that you have not installed the required toolboxes. Please contact [email protected] in case you cannot get the code to work. If you do so, please send along a screenshot of the error, the movie you want to analyze, and details regarding your operating system, matlab version, etc.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Please contact [email protected] for a commercial licence.

Richard Brown (2007) for sharing the circle fit code.

Dongheng, L.,Winfield, D., and Parkhurst, D. J. Starburst: A hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops, 2005, pp. 79–79.

For questions, please contact [email protected]