To enhance the gaze detection system's capability in providing detailed information about the test taker's visual focus, several advancements can be implemented. Firstly, integrating eye-tracking technology that utilizes infrared cameras or specialized hardware could allow for precise tracking of eye movements and pupil dilation. This would enable the system to not only predict gaze direction but also determine the exact screen coordinates where the test taker is focusing.
Additionally, machine learning models could be trained on datasets that include labeled gaze points corresponding to specific screen locations. By employing convolutional neural networks (CNNs) or recurrent neural networks (RNNs), the system could learn to map gaze directions to specific areas on the screen, thus providing a more granular analysis of the test taker's focus.
Furthermore, incorporating a calibration phase at the beginning of the test could improve accuracy. During this phase, the test taker could be asked to look at predetermined points on the screen, allowing the system to adjust for individual differences in eye position relative to the camera. This calibration would enhance the reliability of the gaze data collected throughout the exam, making it easier to identify suspicious behavior, such as looking away to consult external resources.