In this study, we propose a novel method for facial landmark detection (FLD) based on an ensemble of local weighted regressors and a global face shape model under real driving situations. Unlike other FLD approaches, the method proposed in this study first detects the nose region instead of a face-bounding box as a reference point for estimating the offset from a landmark and a reference point. Next, a weighted random forest regressor (WRFR) is used for designing a regressor that maintains the generality while utilizing a small number of decision trees. During the training period, some of the trees having low accuracy are removed and the remaining trees of the WRFR have different weights according to their regression accuracy. As a global face shape model, we use the spatial relationship between three landmarks to identify erroneous estimates of the local regressors and provide valid alternatives. Using the unified framework of the proposed FLD, our algorithm is robust to facial expressions and partial occlusions caused by a subject's hair or sunglasses. For our experiment, using a near-infrared camera, we constructed a benchmark dataset for FLD under real driving situations, which we call the Face Alignment Dataset used In Driving (FADID). The proposed algorithm was successfully applied to various driving sequences in FADID, and the results show that its FLD detection performance is better than that of other state-of-the-art methods.