Driver Facial Landmark Detection in Real Driving Situations

This paper proposes a novel facial landmark detection (FLD) algorithm for use in real driving situations. The proposed algorithm is based on an ensemble of local weighted random forest regressor (WRFR) with random sampling consensus (RANSAC) and explicit global shape models, and considers the dynamic and irregular characteristics of driving. In this paper, to estimate the offset distance from a landmark and a reference point, we first detect the nose region as a reference point. Next, we propose a local WRFR to maintain the generality with a small number of regression trees. With the WRFR, we adopt RANSAC instead of the averaging or median of offsets to handle the problem of sensitivity to outlier offsets, and we estimate the accurate 2D offset vector. To identify the erroneous positions of local landmarks and rearrange the overall landmark layout, we adopt the global face models based on the spatial relation between landmarks. Using the unified framework of the proposed FLD, our proposed algorithm is robust to large head poses and partial occlusions caused by a driver's hair or sunglasses. For a benchmark data set considering real driving situations, we construct a data set called a face alignment data set used in driving (FADID) using a near-infrared camera for FLD under real driving situations. We apply the proposed algorithm to various driving sequences in FADID, and the results show that its FLD performance is better than that of other state-of-the-art methods, while the computational speed is high for real-time applications such as driver-state monitoring systems.