Nonlinear Dynamical Systems for Automatic Face Annotation in Head Tracking and Pose Estimation

TL;DR

This paper compares the performance of Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) in 3D facial motion tracking, revealing their strengths in deterministic and noisy environments respectively.

Contribution

It provides a detailed analysis of EKF and UKF performance in deterministic and stochastic settings for facial tracking, guiding filter selection based on application conditions.

Findings

UKF outperforms EKF in noise-free environments with lower MSE.

EKF shows greater robustness under measurement noise and occlusions.

UKF is preferable for high-precision, controlled environment applications.

Abstract

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.