Abstract
Appearance-based gaze estimation frequently relies on deep Convolutional Neural Networks (CNNs). These models are accurate, but computationally expensive and act as "black boxes", offering little interpretability. Geometric methods based on facial landmarks are a lightweight alternative, but their performance limits and generalization capabilities remain underexplored in modern benchmarks. In this study, we conduct a comprehensive evaluation of landmark-based gaze estimation. We introduce a standardized pipeline to extract and normalize landmarks from three large-scale datasets (Gaze360, ETH-XGaze, and GazeGene) and train lightweight regression models, specifically Extreme Gradient Boosted trees and two neural architectures: a holistic Multi-Layer Perceptron (MLP) and a siamese MLP designed to capture binocular geometry. We find that landmark-based models exhibit lower performance in within-domain evaluation, likely due to noise introduced into the datasets by the landmark detector. Nevertheless, in cross-domain evaluation, the proposed MLP architectures show generalization capabilities comparable to those of ResNet18 baselines. These findings suggest that sparse geometric features encode sufficient information for robust gaze estimation, paving the way for efficient, interpretable, and privacy-friendly edge applications. The source code and generated landmark-based datasets are available at this https URL.
Abstract (translated)
基于外观的注视估计通常依赖于深度卷积神经网络（CNN）。这些模型虽然准确，但计算成本高昂且如同“黑箱模型”，可解释性不足。基于面部标志点的几何方法是一种轻量级替代方案，但其性能极限和泛化能力在现代基准测试中尚未得到充分探索。本研究对基于标志点的注视估计进行了全面评估。我们引入标准化流程，从三个大规模数据集（Gaze360、ETH-XGaze和GazeGene）中提取并归一化标志点，并训练轻量级回归模型，具体包括极端梯度提升树以及两种神经架构：整体式多层感知机（MLP）和用于捕捉双目几何特征的孪生MLP。研究发现，在域内评估中，基于标志点的模型表现较低，这可能源于标志点检测器引入的数据噪声。然而，在跨域评估中，所提出的MLP架构展现出与ResNet18基线相当的泛化能力。这些发现表明，稀疏几何特征编码了足够的信息以实现稳健的注视估计，为高效、可解释且隐私友好型的边缘应用铺平了道路。源代码及生成的基于标志点的数据集可通过此https URL获取。
URL
https://arxiv.org/abs/2603.24724
