  Gaze estimation involves predicting where the person is looking at within an
image or video. Technically, the gaze information can be inferred from two
different magnification levels: face orientation and eye orientation. The
inference is not always feasible for gaze estimation in the wild, given the
lack of clear eye patches in conditions like extreme left/right gazes or
occlusions. In this work, we design a model that mimics humans' ability to
estimate the gaze by aggregating from focused looks, each at a different
magnification level of the face area. The model avoids the need to extract
clear eye patches and at the same time addresses another important issue of
face-scale variation for gaze estimation in the wild. We further extend the
model to handle the challenging task of 360-degree gaze estimation by encoding
the backward gazes in the polar representation along with a robust averaging
scheme. Experiment results on the ETH-XGaze dataset, which does not contain
scale-varying faces, demonstrate the model's effectiveness to assimilate
information from multiple scales. For other benchmark datasets with many
scale-varying faces (Gaze360 and RT-GENE), the proposed model achieves
state-of-the-art performance for gaze estimation when using either images or
videos. Our code and pretrained models can be accessed at
https://github.com/ashesh-0/MultiZoomGaze.
