L2CS-Net
Real-Time Gaze Estimation
RUN
This tool runs the original L2CS-Net gaze estimation model
(ResNet50 backbone) directly in the browser via ONNX Runtime Web. It uses
WebGPU on Chrome / Edge for ~15–20 FPS inference, and falls back
to WASM (CPU) on browsers without WebGPU support.
Compared to the lighter MobileOne-based gaze estimator, L2CS-Net is the
reference architecture from the original paper (Abdelrahman et al., 2022)
and was trained on the Gaze360 dataset for 360° gaze coverage. Faces are
detected with face-api.js, cropped, resized to 448 x 448,
normalised with ImageNet statistics, and decoded into yaw and pitch via the
classic 90-bin softmax-expectation formulation.
Within the Toolkit, it follows the same run workflow as the other JS tools: webcam capture, recording controls, downloadable CSV predictions, and an overview panel summarising the session.
| Output | Description |
|---|---|
yaw_degrees |
Horizontal gaze angle predicted by the L2CS-Net model. |
pitch_degrees |
Vertical gaze angle predicted by the L2CS-Net model. |
face_score |
Confidence score from the face detector used to crop the face. |
face_index |
Per-frame face identifier for multi-person recordings. |