A 2D eye gaze estimation system with low-resolution webcam images

In this article, a low-cost system for 2D eye gaze estimation with low-resolution webcam images is presented. Two algorithms are proposed for this purpose, one for the eye-ball detection with stable approximate pupil-center and the other one for the eye movements' direction detection. Eyeball is detected using deformable angular integral search by minimum intensity (DAISMI) algorithm. Deformable template-based 2D gaze estimation (DTBGE) algorithm is employed as a noise filter for deciding the stable movement decisions. While DTBGE employs binary images, DAISMI employs gray-scale images. Right and left eye estimates are evaluated separately. DAISMI finds the stable approximate pupil-center location by calculating the mass-center of eyeball border vertices to be employed for initial deformable template alignment. DTBGE starts running with initial alignment and updates the template alignment with resulting eye movements and eyeball size frame by frame. The horizontal and vertical deviation of eye movements through eyeball size is considered as if it is directly proportional with the deviation of cursor movements in a certain screen size and resolution. The core advantage of the system is that it does not employ the real pupil-center as a reference point for gaze estimation which is more reliable against corneal reflection. Visual angle accuracy is used for the evaluation and benchmarking of the system. Effectiveness of the proposed system is presented and experimental results are shown.

[1]  Mubarak Shah,et al.  Determining driver visual attention with one camera , 2003, IEEE Trans. Intell. Transp. Syst..

[2]  Robert J. K. Jacob,et al.  Eye tracking in human-computer interaction and usability research : Ready to deliver the promises , 2002 .

[3]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

[4]  Javier Lorenzo-Navarro,et al.  Face and Facial Feature Detection Evaluation - Performance Evaluation of Public Domain Haar Detectors for Face and Facial Feature Detection , 2008, VISAPP.

[5]  Ian R. Fasel,et al.  A generative framework for real time object detection and classification , 2005, Comput. Vis. Image Underst..

[6]  Kang Ryoung Park,et al.  Practical Gaze Point Detecting System , 2004, DAGM-Symposium.

[7]  Hans-Werner Gellersen,et al.  Toward Mobile Eye-Based Human-Computer Interaction , 2010, IEEE Pervasive Computing.

[8]  David Beymer,et al.  What ’ s in the EYES for Attentive Input , 2003 .

[9]  Myung Jin Chung,et al.  A novel non-intrusive eye gaze estimation using cross-ratio under large head motion , 2005, Comput. Vis. Image Underst..

[10]  O.K. Tonguz,et al.  A High Speed Eye Tracking System with Robust Pupil Center Estimation Algorithm , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Dan Witzner Hansen,et al.  Eye tracking in the wild , 2005, Comput. Vis. Image Underst..

[12]  Carlos Hitoshi Morimoto,et al.  Eye gaze tracking techniques for interactive applications , 2005, Comput. Vis. Image Underst..

[13]  Moshe Eizenman,et al.  General theory of remote gaze estimation using the pupil center and corneal reflections , 2006, IEEE Transactions on Biomedical Engineering.

[14]  Shumeet Baluja,et al.  Non-Intrusive Gaze Tracking Using Artificial Neural Networks , 1993, NIPS.

[15]  Terry C. Lansdown,et al.  The mind's eye: cognitive and applied aspects of eye movement research , 2005 .

[16]  Feipei Lai,et al.  Region-based template deformation and masking for eye-feature extraction and description , 1997, Pattern Recognit..

[17]  Peter D. Lawrence,et al.  A non-contact device for tracking gaze in a human computer interface , 2005, Comput. Vis. Image Underst..

[18]  Takeo Kanade,et al.  Dual-state parametric eye tracking , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[19]  Dmitry O. Gorodnichy,et al.  On importance of nose for face tracking , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[20]  Tommy Strandvall,et al.  Eye Tracking in Human-Computer Interaction and Usability Research , 2009, INTERACT.

[21]  Andreas Paepcke,et al.  Improving the accuracy of gaze input for interaction , 2008, ETRA.

[22]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.