Investigation of Architectures and Receptive Fields for Appearance-based
  Gaze Estimation
Yunhan Wang Xiangwei Shi Shalini De Mello Hyung Jin Chang Xucong Zhang
With the rapid development of deep learning technology in the past decade,
appearance-based gaze estimation has attracted great attention from both
computer vision and human-computer interaction research communities.
Fascinating methods were proposed with variant mechanisms including soft
attention, hard attention, two-eye asymmetry, feature disentanglement, rotation
consistency, and contrastive learning. Most of these methods take the
single-face or multi-region as input, yet the basic architecture of gaze
estimation has not been fully explored. In this paper, we reveal the fact that
tuning a few simple parameters of a ResNet architecture can outperform most of
the existing state-of-the-art methods for the gaze estimation task on three
popular datasets. With our extensive experiments, we conclude that the stride
number, input image resolution, and multi-region architecture are critical for
the gaze estimation performance while their effectiveness dependent on the
quality of the input face image. We obtain the state-of-the-art performances on
three datasets with 3.64 on ETH-XGaze, 4.50 on MPIIFaceGaze, and 9.13 on
Gaze360 degrees gaze estimation error by taking ResNet-50 as the backbone.