Analysis of different threshold selection methods for eye image segmentation used in eye tracking applications

The purpose of this paper is to compare two established threshold selection methods used for eye image segmentation: quantitative fixed threshold and Kittler minimum error threshold with a new one based on characteristic selection adaptive threshold. This method relies on identifying the characteristic that describes the pixels that belong to the eye pupil. The results are obtained by using as reference the real pupil contour that was manually obtained by analyzing a number of 52 infrared images captured from a set of healthy individuals in laboratory condition.

[1]  Radu Gabriel Bozomitu,et al.  Implementation of eye-tracking system based on circular Hough transform algorithm , 2015, 2015 E-Health and Bioengineering Conference (EHB).

[2]  Bang Jun Lei,et al.  Median-based thresholding, minimum error thresholding, and their relationships with histogram-based image similarity , 2014, Digital Image Processing.

[3]  Yanggon Kim,et al.  Pupil and Iris Localization for Iris Recognition in Mobile Phones , 2006, Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD'06).

[4]  Abhaya Indrayan Medical Biostatistics, Third Edition , 2012 .

[5]  Gurjeet kaur Seerha Review on Recent Image Segmentation Techniques , 2013 .

[6]  Mariusz Zubert,et al.  Reliable algorithm for iris segmentation in eye image , 2010, Image Vis. Comput..

[7]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..

[8]  Isaak Kavasidis,et al.  Improving mobile device interaction by eye tracking analysis , 2012, 2012 Federated Conference on Computer Science and Information Systems (FedCSIS).

[9]  Weixing Wang,et al.  Driver Fatigue Detection Based on Eye Tracking , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[10]  Dongheng Li,et al.  Starburst: A hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[11]  Cornelius T. Leondes Image processing and pattern recognition , 1998 .

[12]  Lianghai Jin,et al.  Characteristic analysis of Otsu threshold and its applications , 2011, Pattern Recognit. Lett..

[13]  Olive Jean Dunn Basic Statistics: A Primer for the Biomedical Sciences. , 1965 .

[14]  Mariano Ruiz Espejo Medical biostatistics, third edition , 2014 .

[15]  Jan J. Gerbrands,et al.  Objective and quantitative segmentation evaluation and comparison , 1994, Signal Process..

[16]  N Otsu,et al.  An automatic threshold selection method based on discriminate and least squares criteria , 1979 .