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.

提出实时框架及卷积神经网络用于分类人眼凝视方向和评估眼部访问线索，并使用改进的 Viola-Jones 算法检测人脸，通过几何关系和面部特征点得到眼部区域，并进行逐帧测试以获得平均帧速率 24fps，结果显示其优于现有方法。