Gaze estimation using convolutional neural networks

Numerous investigations on gaze estimate techniques for analyzing human behavior have been made in recent years, the majority of which have focused on gaze tracking techniques. This article proposes a new method for gaze estimation. The proposed system is divided into three phases: (i) estimation of head position using convolutional neural networks (CNN) (VGG16, Resnet50, InceptionV3), (ii) detection of eyes area using Viola Jones' algorithm, and in phase (iii) gaze estimation using three different models: pre-trained CNN, CNN from scratch, as well as bilinear convolutional neural networks. Columbia gaze database is used in the validation experiments. The results obtained from our novel gaze estimation method exhibit significantly improved accuracy compared to the existing approaches in the same field, which are limited in number.