论文标题
L2CS网络：无约束环境中的细粒度凝视估计
L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments
论文作者
论文摘要
人类目光是一种至关重要的提示，用于各种应用，例如人类机器人互动和虚拟现实。最近，卷积神经网络（CNN）方法在预测凝视方向方面取得了显着进步。但是，由于眼睛外观，闪电条件以及头姿势和凝视方向的多样性，估计凝视在野外的问题仍然是一个具有挑战性的问题。在本文中，我们提出了一个基于CNN的强大模型，用于预测不受限制的设置中的凝视。我们建议分别回归每个凝视角度，以提高人均预测准确性，这将提高整体凝视性能。此外，我们使用两个相同的损失，一个针对每个角度，以改善网络学习并增加其概括。我们使用两个流行的数据集评估了我们的模型，这些数据集收集了没有受限的设置。我们提出的模型分别在Mpiigaze和Gaze360数据集上实现了3.92°和10.41°的最新精度。我们在https://github.com/ahmednull/l2cs-net上进行代码开源。
Human gaze is a crucial cue used in various applications such as human-robot interaction and virtual reality. Recently, convolution neural network (CNN) approaches have made notable progress in predicting gaze direction. However, estimating gaze in-the-wild is still a challenging problem due to the uniqueness of eye appearance, lightning conditions, and the diversity of head pose and gaze directions. In this paper, we propose a robust CNN-based model for predicting gaze in unconstrained settings. We propose to regress each gaze angle separately to improve the per-angel prediction accuracy, which will enhance the overall gaze performance. In addition, we use two identical losses, one for each angle, to improve network learning and increase its generalization. We evaluate our model with two popular datasets collected with unconstrained settings. Our proposed model achieves state-of-the-art accuracy of 3.92° and 10.41° on MPIIGaze and Gaze360 datasets, respectively. We make our code open source at https://github.com/Ahmednull/L2CS-Net.
