Design of eye control human–computer interaction interface based on smooth tracking

A deep learning model based on smooth tracking, the adaptive lightweight eye control tracking Transformer, is proposed to address the accuracy and real-time issues of eye control human–computer interaction interfaces. By utilizing convolutional neural network and recurrent neural network frameworks, the adaptive lightweight eye control tracking Transformer demonstrates excellent performance through experimental validation on EYEDIAP, DUT, and GazeCapture datasets. On the EYEDIAP dataset, the raised model achieved a curve area value of 0.934 and specificity of 0.902, with a mean absolute error of only 0.065 and an average inference time of 12.453 ms. On the DUT dataset, the area under the curve value is 0.917, the specificity is 0.895, the mean absolute error is 0.062, and the inference time is 11.789 ms. The best performance is achieved on the GazeCapture dataset, with an area under the curve value of 0.944, specificity of 0.910, mean absolute error of 0.056, and inference time of 10.756 ms. The research findings denote that the raised model has significant merits in raising the accuracy and response speed of eye control interaction, providing new possibilities for the application of eye control technology in fields such as virtual reality, education and training, and health monitoring. A lightweight, adaptive eye-tracking model, ALETT, is introduced for improved human-computer interaction. ALETT demonstrates high accuracy and low inference time across multiple datasets, including EYEDIAP and GazeCapture. The proposed method has advantages in improving the accuracy and response speed of eye control interaction.