Abstract
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 linear relationships between gaze directions in different domains remain 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.
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Acknowledgement
This work was supported by the National Key R&D Program of China (2016YFB 1001001), the National Natural Science Foundation of China (61976170, 91648121, 61573280), and Tencent Robotics X Lab Rhino-Bird Joint Research Program (201902, 201903). (Portions of) the research in this paper used the EYEDIAP dataset made available by the Idiap Research Institute, Martigny, Switzerland.
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Guo, Z., Yuan, Z., Zhang, C., Chi, W., Ling, Y., Zhang, S. (2021). Domain Adaptation Gaze Estimation by Embedding with Prediction Consistency. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12626. Springer, Cham. https://doi.org/10.1007/978-3-030-69541-5_18
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