  Estimation eye gaze direction is useful in various human-computer interaction
tasks. Knowledge of gaze direction can give valuable information regarding
users point of attention. Certain patterns of eye movements known as eye
accessing cues are reported to be related to the cognitive processes in the
human brain. We propose a real-time framework for the classification of eye
gaze direction and estimation of eye accessing cues. In the first stage, the
algorithm detects faces using a modified version of the Viola-Jones algorithm.
A rough eye region is obtained using geometric relations and facial landmarks.
The eye region obtained is used in the subsequent stage to classify the eye
gaze direction. A convolutional neural network is employed in this work for the
classification of eye gaze direction. The proposed algorithm was tested on Eye
Chimera database and found to outperform state of the art methods. The
computational complexity of the algorithm is very less in the testing phase.
The algorithm achieved an average frame rate of 24 fps in the desktop
environment.
