A novel particle filter, the memory-based particle filter (M-PF), is proposed that can visually track moving objects that have complex dynamics. We aim to realize robustness against abrupt object movements and quick recovery from tracking failure caused by factors such as occlusions. To that end, we eliminate the Markov assumption from the previous particle filtering framework and predict the prior distribution of the target state from the long-term dynamics. More concretely, M-PF stores the past history of the estimated target states, and employs a random sampling from the history to generate prior distribution; it represents a novel PF formulation.Our method can handle nonlinear, time-variant, and non-Markov dynamics, which is not possible within existing PF frameworks. Accurate prior prediction based on proper dynamics model is especially effective for recovering lost tracks, because it can provide possible target states, which can drastically change since the track was lost. We target the face pose of seated humans in this paper. Quantitative evaluations with magnetic sensors confirm improved accuracy in face pose estimation and successful recovery from tracking loss. The proposed M-PF suggests a new paradigm for modeling systems with complex dynamics and so offers a various visual tracking applications.