Eye blinking detection to perform selection for an eye tracking system used in assistive technology

The paper presents the analysis of methods used to implement eye blinking selection in an eye tracking system used in assistive technology for patients with neuro-motor disabilities. The system uses for selection the key words technology or ideograms presented on the user's screen. The method used for blinking detection is based on image segmentation using an adaptive local threshold determined using the integral sum image or Bradley method. Results obtained present that the method implemented for blinking detection can be used efficiently in a real time eye tracking system for assistive technology applications.

[1]  Albert Chi,et al.  A pilot study of eye-tracking devices in intensive care. , 2016, Surgery.

[2]  M. Aramideh,et al.  Eyelid movements: behavioral studies of blinking in humans under different stimulus conditions. , 2003, Journal of neurophysiology.

[3]  Y. V. Venkatesh,et al.  Blink detection and eye tracking for eye localization , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.

[4]  Minoru Ohyama,et al.  Automatic Classification Between Involuntary and Two Types of Voluntary Blinks Based on an Image Analysis , 2015, HCI.

[5]  C. Rotariu,et al.  Pupil detection algorithms for eye tracking applications , 2015, 2015 IEEE 21st International Symposium for Design and Technology in Electronic Packaging (SIITME).

[6]  Bernard R. Rosner,et al.  Fundamentals of Biostatistics. , 1992 .

[7]  Noureddine Cherabit,et al.  Circular Hough Transform for Iris localization , 2012 .

[8]  Sudipta Roy,et al.  A New Local Adaptive Thresholding Technique in Binarization , 2012, ArXiv.

[9]  Derek Bradley,et al.  Adaptive Thresholding using the Integral Image , 2007, J. Graph. Tools.

[10]  Margrit Betke,et al.  Real Time Eye Tracking and Blink Detection with USB Cameras , 2005 .

[11]  Michael Burch,et al.  Visual Analytics Methodology for Eye Movement Studies , 2012, IEEE Transactions on Visualization and Computer Graphics.

[12]  Boris B. Velichkovsky,et al.  New Solution to the Midas Touch Problem: Identification of Visual Commands Via Extraction of Focal Fixations , 2014, IHCI.

[13]  Andrew T. Duchowski,et al.  Eye Tracking Techniques , 2003 .

[14]  Antonio Nardone,et al.  Eye tracking communication devices in amyotrophic lateral sclerosis: Impact on disability and quality of life , 2013, Amyotrophic lateral sclerosis & frontotemporal degeneration.

[15]  Shyam Prasad,et al.  Gaze and blinking base human machine interaction system , 2015, 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC).

[16]  Laura Chamberlain.  Eye Tracking Methodology; Theory and Practice , 2007 .

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

[18]  ALSUntangled,et al.  Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration , 2013 .

[19]  Konstantinos G. Derpanis,et al.  Integral image-based representations , 2007 .