用偏振光和孪生网络，少样本实现高精度眼动追踪。
Polarization-Based Eye Tracking with Personalized Siamese Architectures
- 基于孪生网络学习个体眼动偏移，用少量校准数据重建准确注视点。
- 仅需1/10样本即达线性校准效果，偏振输入误差比近红外低12%。
- 结合线性校准可再降误差13%，适合无感校准场景。
集成眼动追踪的头戴设备有望实现自然人机交互，但通常需针对每位用户校准以应对个体差异。本文提出一种基于孪生架构的差异化个性化方法，通过少量校准帧学习相对注视偏移，并重构绝对注视位置。在包含338名受试者、使用偏振敏感相机与850 nm照明采集的数据集上进行基准测试，结果表明：该方法仅需线性校准十分之一的样本即可达到相当性能；采用偏振输入相较近红外输入，注视误差最多降低12%；将孪生个性化与线性校准结合，相比线性校准基线最多提升13%。这些结果确立了孪生个性化在实际眼动追踪中的可行性与有效性。
原文摘要 · Abstract (English)
Head-mounted devices integrated with eye tracking promise a solution for natural human-computer interaction. However, they typically require per-user calibration for optimal performance due to inter-person variability. A differential personalization approach using Siamese architectures learns relative gaze displacements and reconstructs absolute gaze from a small set of calibration frames. In this paper, we benchmark Siamese personalization on polarization-enabled eye tracking. For benchmarking, we use a 338-subject dataset captured with a polarization-sensitive camera and 850 nm illumination. We achieve performance comparable to linear calibration with 10-fold fewer samples. Using polarization inputs for Siamese personalization reduces gaze error by up to 12% compared to near-infrared (NIR)-based inputs. Combining Siamese personalization with linear calibration yields further improvements of up to 13% over a linearly calibrated baseline. These results establish Siamese personalization as a practical approach enabling accurate eye tracking.
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