  Gaze estimation, which is a method to determine where a person is looking at
given the person's full face, is a valuable clue for understanding human
intention. Similarly to other domains of computer vision, deep learning (DL)
methods have gained recognition in the gaze estimation domain. However, there
are still gaze calibration problems in the gaze estimation domain, thus
preventing existing methods from further improving the performances. An
effective solution is to directly predict the difference information of two
human eyes, such as the differential network (Diff-Nn). However, this solution
results in a loss of accuracy when using only one inference image. We propose a
differential residual model (DRNet) combined with a new loss function to make
use of the difference information of two eye images. We treat the difference
information as auxiliary information. We assess the proposed model (DRNet)
mainly using two public datasets (1) MpiiGaze and (2) Eyediap. Considering only
the eye features, DRNet outperforms the state-of-the-art gaze estimation
methods with $angular-error$ of 4.57 and 6.14 using MpiiGaze and Eyediap
datasets, respectively. Furthermore, the experimental results also demonstrate
that DRNet is extremely robust to noise images.
