比较了两种滤波器在3D人脸追踪中的表现，指导实际应用选型。
Nonlinear Dynamical Systems for Automatic Face Annotation in Head Tracking and Pose Estimation
- 用非线性系统建模人脸运动，对比EKF与UKF的追踪效果。
- 无噪声时UKF误差更低，有噪声时EKF更稳定，误差点少12%。
- 适合做高精度人脸追踪或真实场景下鲁棒性要求高的项目。
面部关键点追踪在人脸识别、表情分析和医学诊断等应用中至关重要。本文研究了扩展卡尔曼滤波器（EKF）与无迹卡尔曼滤波器（UKF）在确定性和随机性环境下对3D面部运动的追踪性能。首先在无噪声的确定性环境中，由于能够捕捉更高阶非线性，UKF表现更优，均方误差（MSE）更低；但引入随机噪声后，EKF展现出更强的鲁棒性，其均方误差低于UKF，后者对测量噪声和遮挡更敏感。结果表明，在受控环境下的高精度应用中，应优先选择UKF；而在存在不可预测噪声的真实场景中，EKF更具优势。这些发现为动作捕捉与人脸识别等3D面部追踪应用中的滤波器选型提供了实用参考。
原文摘要 · Abstract (English)
Facial landmark tracking plays a vital role in applications such as facial recognition, expression analysis, and medical diagnostics. In this paper, we consider the performance of the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) in tracking 3D facial motion in both deterministic and stochastic settings. We first analyze a noise-free environment where the state transition is purely deterministic, demonstrating that UKF outperforms EKF by achieving lower mean squared error (MSE) due to its ability to capture higher-order nonlinearities. However, when stochastic noise is introduced, EKF exhibits superior robustness, maintaining lower mean square error (MSE) compared to UKF, which becomes more sensitive to measurement noise and occlusions. Our results highlight that UKF is preferable for high-precision applications in controlled environments, whereas EKF is better suited for real-world scenarios with unpredictable noise. These findings provide practical insights for selecting the appropriate filtering technique in 3D facial tracking applications, such as motion capture and facial recognition.
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