Real-time Eye Gaze Direction Classification Using Convolutional Neural Network
This work addresses the problem of real-time gaze estimation for human-computer interaction, but it appears incremental as it builds on existing methods like Viola-Jones and CNNs.
The authors tackled real-time eye gaze direction classification using a convolutional neural network, achieving an average frame rate of 24 fps and outperforming state-of-the-art methods on the Eye Chimera database.
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.