EM-Net: Gaze Estimation with Expectation Maximization Algorithm
This work addresses computational efficiency for gaze estimation applications, but it is incremental as it builds on existing methods with hybrid techniques.
The paper tackles the problem of high computational resource demands in gaze estimation by proposing EM-Net, a lightweight model that improves performance by 2.2%, 2.02%, and 2.03% on three datasets using only 50% of training data compared to GazeNAS-ETH.
In recent years, the accuracy of gaze estimation techniques has gradually improved, but existing methods often rely on large datasets or large models to improve performance, which leads to high demands on computational resources. In terms of this issue, this paper proposes a lightweight gaze estimation model EM-Net based on deep learning and traditional machine learning algorithms Expectation Maximization algorithm. First, the proposed Global Attention Mechanism(GAM) is added to extract features related to gaze estimation to improve the model's ability to capture global dependencies and thus improve its performance. Second, by learning hierarchical feature representations through the EM module, the model has strong generalization ability, which reduces the need for sample size. Experiments have confirmed that, on the premise of using only 50% of the training data, EM-Net improves the performance of Gaze360, MPIIFaceGaze, and RT-Gene datasets by 2.2%, 2.02%, and 2.03%, respectively, compared with GazeNAS-ETH. It also shows good robustness in the face of Gaussian noise interference.