![]() Fine-tuning was also performed using the VGG16 pre-trained model. Different experiments were performed, and the experimental result reveals that the proposed technique outperforms the second model. The CNN model accepts only the face images as input. Another CNN model was designed in this study primarily for the sake of comparison. Adding this information can make the network learn free-head and eye movements. The face component is used to extract the gaze estimation features from the eyes, while the 39-point facial landmark component is used to encode the shape and location of the eyes (within the face) into the network. The proposed technique consists of two components, namely a face component and a 39-point facial landmark component. To address these issues, this study proposes a Convolutional Neural Network (CNN) based calibration-free technique for improved gaze estimation in unconstrained environments. Explicit personal calibration can also be cumbersome and degrades the user experience. Some of them necessitate explicit personal calibration, which makes them unsuitable for use in real-world or uncontrolled environments. Many eye tracking technologies are currently expensive and only available to large corporations. Department of Computer Science and Informatics, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein, South AfricaĮye tracking is becoming a very popular, useful, and important technology.
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