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.

本研究解决了现有注视估计方法对大规模数据集和模型的依赖带来的高计算资源需求问题。提出的轻量级模型EM-Net结合深度学习和期望最大化算法，通过全局注意机制提取相关特征，并通过EM模块学习层次特征表示，显著提高了模型的泛化能力。实验表明，在仅使用50%训练数据的情况下，EM-Net在多个数据集上的性能均有提升，同时在高斯噪声干扰下表现出良好的鲁棒性。

EM-Net：基于期望最大化算法的注视估计