A novel convolutional feature-based method for predicting limited mobility eye gaze direction

Abstract

Eye gaze direction is a critical issue since several applications in computer vision technology rely on determining gaze direction, where individuals move their eyes to limited mobility locations for sensory information. Deep neural networks are considered one of the most essential and accurate image classification methods. Several methods of classification to determine the direction of the gaze employ convolutional neural network models, which are VGG, ResNet, Alex Net, etc. This research presents a new method of identifying human eye images and classifying eye gaze directions (left, right, up, down, straight) in addition to eye-closing discrimination. The proposed method (Di-eyeNET) stands out from the developed method (Split-HSV) for enhancing image lighting. It also reduces implementation time by utilizing only two blocks and employing dropout layers after each block to achieve fast response times and high accuracy. It focused on the characteristics of the human eye images, as it is small, so it cannot be greatly enlarged, and the eye's iris is in the middle of the image, so the edges are not important. The proposed method achieves excellent results compared to previous methods, classifying the five directions of eye gaze instead of the four directions. Both the global dataset and the built local dataset were utilized. Compared to previous methods, the suggested method's results demonstrate high accuracy (99%), minimal loss, and the lowest training time. The research benefits include an efficient method for classifying eye gaze directions, with faster implementation and improved image lighting.

Keywords

• convolutional neural networks
• computer vision
• eye gaze
• haar cascade
• iris