论文标题
野外基于外观的目光估计的联合学习
Federated Learning for Appearance-based Gaze Estimation in the Wild
论文作者
论文摘要
近年来，凝视估计方法已经显着成熟，但是训练深度学习模型所需的大量眼睛图像带来了很大的隐私风险。此外，不同用户的异质数据分布可能会极大地阻碍培训过程。在这项工作中，我们提出了第一种凝视估算的联合学习方法，以保留凝视数据的隐私。我们进一步采用伪级优化，以使我们的联合学习方法适应不同的模型更新，以在协作设置中解决野外凝视数据的异质性质。我们在现实世界中的数据集（Mpiigigaze）上评估了我们的方法，并表明我们的工作增强了基于外观的凝视估计方法的隐私保证，处理凝视估计器的收敛问题，并显着超过了香草的融合，从15.8％（平均误差）（平均误差为10.63摄氏度至8.95摄氏度））。因此，我们的工作为开发隐私感知的协作学习设置铺平了道路，以进行凝视，同时保持模型的性能。
Gaze estimation methods have significantly matured in recent years, but the large number of eye images required to train deep learning models poses significant privacy risks. In addition, the heterogeneous data distribution across different users can significantly hinder the training process. In this work, we propose the first federated learning approach for gaze estimation to preserve the privacy of gaze data. We further employ pseudo-gradient optimisation to adapt our federated learning approach to the divergent model updates to address the heterogeneous nature of in-the-wild gaze data in collaborative setups. We evaluate our approach on a real-world dataset (MPIIGaze) and show that our work enhances the privacy guarantees of conventional appearance-based gaze estimation methods, handles the convergence issues of gaze estimators, and significantly outperforms vanilla federated learning by 15.8% (from a mean error of 10.63 degrees to 8.95 degrees). As such, our work paves the way to develop privacy-aware collaborative learning setups for gaze estimation while maintaining the model's performance.
