Gaze estimation using Convolutional Neural Networks

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Abstract 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 Con-volutional 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 (B-CNNs). Columbia gaze database is used in the validation experiments. When compared to earlier efforts, the experimental results demonstrate that the proposed method produces a more exact outcome.

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