Real-time Eye Gaze Direction Classification Using Convolutional Neural Network

TL;DR

This paper presents a real-time CNN-based framework for classifying eye gaze direction and estimating eye accessing cues, enhancing human-computer interaction with high accuracy and low computational complexity.

Contribution

It introduces a novel real-time eye gaze classification method combining face detection, geometric eye region extraction, and CNN, outperforming existing methods.

Findings

Achieved 24 fps in real-time classification

Outperformed state-of-the-art methods on Eye Chimera database

Low computational complexity during testing

Abstract

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.