Information extraction of facial expressions deals with facial-feature detection, feature tracking, and capture of the spatiotemporal relationships among features. It is a fundamental task in facial expression analysis and will ultimately determine the performance of expression recognition. For a real-world facial expression sequence, there are three challenges: (1) detection failure of some or all facial features due to changes in illumination and rapid head movement; (2) nonrigid object tracking resulting from facial expression change; and (3) feature occlusion due to out-of-plane head rotation. In this paper, a new approach is proposed to tackle these challenges. First, we use an active infrared (IR) illumination to reliably detect pupils under variable lighting conditions and head orientations. The pupil positions are then used to guide the entire information-extraction process. The simultaneous use of a global head motion constraint and Kalman filtering can robustly track individual facial features even in condition of rapid head motion and significant expression change. To handle feature occlusion, we propose a warping-based reliability propagation method. The reliable neighbor features and the spatial semantics among these features are used to detect and infer occluded features through an interframe warping transformation. Experimental results show that accurate information extraction can be achieved for video sequences with real-world facial expressions.