Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gaze-Net: Appearance-Based Gaze Estimation using Capsule Networks   Recent studies on appearance based gaze estimation indicate the ability of
Neural Networks to decode gaze information from facial images encompassing pose
information. In this paper, we propose Gaze-Net: A capsule network capable of
decoding, representing, and estimating gaze information from ocular region
images. We evaluate our proposed system using two publicly available datasets,
MPIIGaze (200,000+ images in the wild) and Columbia Gaze (5000+ images of users
with 21 gaze directions observed at 5 camera angles/positions). Our model
achieves a Mean Absolute Error (MAE) of 2.84$^\circ$ for Combined angle error
estimate within dataset for MPI-IGaze dataset. Further, model achieves a MAE of
10.04$^\circ$ for across dataset gaze estimation error for Columbia gaze
dataset. Through transfer learning, the error is reduced to 5.9$^\circ$. The
results show this approach is promising with implications towards using
commodity webcams to develop low-cost multi-user gaze tracking systems.

11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex