  Gaze is the essential manifestation of human attention. In recent years, a
series of work has achieved high accuracy in gaze estimation. However, the
inter-personal difference limits the reduction of the subject-independent gaze
estimation error. This paper proposes an unsupervised method for domain
adaptation gaze estimation to eliminate the impact of inter-personal diversity.
In domain adaption, we design an embedding representation with prediction
consistency to ensure that the linear relationship between gaze directions in
different domains remains consistent on gaze space and embedding space.
Specifically, we employ source gaze to form a locally linear representation in
the gaze space for each target domain prediction. Then the same linear
combinations are applied in the embedding space to generate hypothesis
embedding for the target domain sample, remaining prediction consistency. The
deviation between the target and source domain is reduced by approximating the
predicted and hypothesis embedding for the target domain sample. Guided by the
proposed strategy, we design Domain Adaptation Gaze Estimation Network(DAGEN),
which learns embedding with prediction consistency and achieves
state-of-the-art results on both the MPIIGaze and the EYEDIAP datasets.
