Enhanced eye gaze direction classification using a combination of face detection, CHT and SVM

Automatic estimation of eye gaze direction is an interesting research area in the field of computer vision that is growing rapidly with its wide range of potential applications. However, it is still a very challenging task to implement a robust eye gaze classification system. This paper proposes a robust eye detection system that uses face detection for finding the eyes region. The Circular Hough Transform (CHT) is used for locating the center of the iris. The parameters of the Circular Hough Transform are dynamically calculated based on the detected face information. A new method for eye gaze direction classification using Support Vector Machine (SVM) is introduced and combined with Circular Hough Transform to complete the task required. The experiments were performed on a database containing 4000 images of 40 subjects from different ages and genders. The algorithm achieved a classification accuracy of up to 92.1%.

[1]  Preeti R. Bajaj,et al.  Driver Drowsiness Detection Using Skin Color Algorithm and Circular Hough Transform , 2011, 2011 Fourth International Conference on Emerging Trends in Engineering & Technology.

[2]  Driss Aboutajdine,et al.  Eye state analysis using iris detection based on Circular Hough Transform , 2011, 2011 International Conference on Multimedia Computing and Systems.

[3]  Myung Jin Chung,et al.  Non-contact eye gaze tracking system by mapping of corneal reflections , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[4]  T. Vilis,et al.  Geometric relations of eye position and velocity vectors during saccades , 1990, Vision Research.

[5]  Mehrübe Mehrübeoglu,et al.  Real-time eye tracking using a smart camera , 2011, 2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

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

[7]  S. Pizer,et al.  The Image Processing Handbook , 1994 .

[8]  Takehiko Ohno,et al.  One-point calibration gaze tracking method , 2006, ETRA.

[9]  David Beymer,et al.  Eye gaze tracking using an active stereo head , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[11]  Shumin Zhai,et al.  Keeping an eye for HCI , 1999, XII Brazilian Symposium on Computer Graphics and Image Processing (Cat. No.PR00481).

[12]  Wei Lu,et al.  Application of hough transform in eye tracking and targeting , 2009, 2009 9th International Conference on Electronic Measurement & Instruments.

[13]  Ravi Kothari,et al.  Detection of eye locations in unconstrained visual images , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[14]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

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

[16]  Chern-Sheng Lin,et al.  POLAR COORDINATE MAPPING METHOD FOR AN IMPROVED INFRARED EYE-TRACKING SYSTEM , 2005 .