Application of head flexion detection for enhancing eye gaze direction classification

Extensive research has been conducted on the tracking and detection of the eye gaze and head movement detection as these aspects of technology can be applied as alternative approaches for various interfacing devices. This paper proposes enhancements to the classification of the eye gaze direction. Viola Jones face detector is applied to first declare the region of the eye. Circular Hough Transform is then used to detect the iris location. Support Vector Machine (SVM) is applied to classify the eye gaze direction. Accuracy of the system is enhanced by calculating the flexion angle of the head through the utilization of a microcontroller and flex sensors. In case of rotated face images, the face can be rotated back to zero degrees through the flexion angle calculation. This is while Viola Jones face detector is limited to face images with very little or no rotation angle. Accuracy is initiated by enhancing the effectiveness of the system in the overall procedure of classifying the direction of the eye gaze. Therefore, the head direction is a main determinant in enhancing the control method. Different control signals are enhanced by the eye gaze direction classification and the head direction detection.

[1]  Abidhusain Syed,et al.  Flex Sensor Based Robotic Arm Controller Using Micro Controller , 2012 .

[2]  Yoshinobu Ebisawa,et al.  PupilMouse supported by head pose detection , 2008, 2008 IEEE Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems.

[3]  Miad Faezipour,et al.  Enhanced eye gaze direction classification using a combination of face detection, CHT and SVM , 2013, 2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).

[4]  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.

[5]  Young-il Kim,et al.  Construction of integrated simulator for developing head/eye tracking system , 2008, 2008 International Conference on Control, Automation and Systems.

[6]  James Gips,et al.  Using EagleEyes—an electrodes based device for controlling the computer with your eyes—to help people with special needs , 1996 .

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

[8]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[9]  Hong Liu,et al.  Robust real-time eye detection and tracking for rotated facial images under complex conditions , 2010, 2010 Sixth International Conference on Natural Computation.

[10]  Kun Liu,et al.  Attention recognition of drivers based on head pose estimation , 2008, 2008 IEEE Vehicle Power and Propulsion Conference.

[11]  Sergios Theodoridis,et al.  Pattern Recognition , 1998, IEEE Trans. Neural Networks.

[12]  Yoichi Sato,et al.  Estimating change in head pose from low resolution video using LBP-based tracking , 2011, 2011 International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS).

[13]  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..