We present a novel approach for automatically detecting and tracking facial landmarks across poses and expressions from in-the-wild monocular video data, e.g., YouTube videos and smartphone recordings. Our method does not require any calibration or manual adjustment for new individual input videos or actors. Firstly, we propose a method of robust 2D facial landmark detection across poses, by combining shape-face canonical-correlation analysis with a global supervised descent method. Since 2D regression-based methods are sensitive to unstable initialization, and the temporal and spatial coherence of videos is ignored, we utilize a coarse-to-dense 3D facial expression reconstruction method to refine the 2D landmarks. On one side, we employ an in-the-wild method to extract the coarse reconstruction result and its corresponding texture using the detected sparse facial landmarks, followed by robust pose, expression, and identity estimation. On the other side, to obtain dense reconstruction results, we give a face tracking flow method that corrects coarse reconstruction results and tracks weakly textured areas; this is used to iteratively update the coarse face model. Finally, a dense reconstruction result is estimated after it converges. Extensive experiments on a variety of video sequences recorded by ourselves or downloaded from YouTube show the results of facial landmark detection and tracking under various lighting conditions, for various head poses and facial expressions. The overall performance and a comparison with state-of-art methods demonstrate the robustness and effectiveness of our method.