CI-Net: Appearance-Based Gaze Estimation via Cooperative Network

Facial occlusion and different appearances of both eyes in natural scenes can affect the accuracy of gaze estimation based on appearance. Therefore, this paper proposes a gaze estimation model based on cooperative network: CI-Net, including a consistency estimation network (C-Net) and inconsistency...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.78739-78746
Main Authors: Luo, Yuan, Chen, Jiangtao, Chen, Jian
Format: Article
Language:English
Subjects:
Convolutional neural networks
Cooperative systems
Deep learning
Estimation
Eye (anatomy)
Feature extraction
Gaze estimation
Gaze tracking
main gaze
Occlusion
residual residuals
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Summary:Facial occlusion and different appearances of both eyes in natural scenes can affect the accuracy of gaze estimation based on appearance. Therefore, this paper proposes a gaze estimation model based on cooperative network: CI-Net, including a consistency estimation network (C-Net) and inconsistency estimation network (I-Net). C-Net is used to estimate the Main gaze of the true gaze, and an attention mechanism is added to adaptively assign the weight between eyes and face features. The I-Net is used to estimate the Residual residuals based on true gaze. In addition, Cross attention module is designed in this paper, through which I-Net can selectively obtain information from C-Net, to obtain more accurate eyes directions. The experimental results in this paper show that the CI-Net gain lower angle errors than the current mainstream CNN methods under the condition of different appearance of both eyes and facial occlusion.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3194123