EFE: End-to-end Frame-to-Gaze Estimation
Haldun Balim Seonwook Park Xi Wang Xucong Zhang Otmar Hilliges
Despite the recent development of learning-based gaze estimation methods,
most methods require one or more eye or face region crops as inputs and produce
a gaze direction vector as output. Cropping results in a higher resolution in
the eye regions and having fewer confounding factors (such as clothing and
hair) is believed to benefit the final model performance. However, this
eye/face patch cropping process is expensive, erroneous, and
implementation-specific for different methods. In this paper, we propose a
frame-to-gaze network that directly predicts both 3D gaze origin and 3D gaze
direction from the raw frame out of the camera without any face or eye
cropping. Our method demonstrates that direct gaze regression from the raw
downscaled frame, from FHD/HD to VGA/HVGA resolution, is possible despite the
challenges of having very few pixels in the eye region. The proposed method
achieves comparable results to state-of-the-art methods in Point-of-Gaze (PoG)
estimation on three public gaze datasets: GazeCapture, MPIIFaceGaze, and EVE,
and generalizes well to extreme camera view changes.