The iMotions WebET 3.0 white paper offers a comprehensive overview of their latest webcam-based eye tracking algorithm. This groundbreaking technology utilizes deep learning networks to accurately estimate gaze positions from webcam videos, transforming standard webcams into powerful eye tracking devices.
Key highlights include:
- Three-Step Algorithm: The WebET 3.0 operates through a sophisticated three-step process involving processing, calibration, and mapping, ensuring precise and reliable eye tracking.
- Deep Learning Models: These models, pre-trained on thousands of faces, are at the core of the WebET 3.0, enabling it to accurately process and interpret eye movement data.
- Enhanced Calibration: Incorporating non-linear RBF-based regression models, the calibration step is crucial for matching gaze positions to expected locations, improving overall accuracy.
- Robust Mapping Capabilities: The final step maps gaze points to screen coordinates, allowing detailed analysis of eye movement across various stimuli.
- Recommended Setups: The white paper also provides essential setup recommendations for maximizing the performance of the WebET 3.0, including optimal stimuli presentation and calibration techniques.
This white paper is an essential read for professionals in user experience research, psychology, and other fields where eye tracking is pivotal, offering insights into the advanced capabilities and applications of iMotions’ latest innovation in eye tracking technology.
