## 2 Related Work  
Typically, head pose estimation is the first step in many driving safety applications. The head pose estimation methods can be categorized into visual methods, which monitor the position of face, its expression, and various movements of its parts, such as blinking or yawning. On the other hand, multimodal methods combine information from driver videos or images with additional data obtained from physiological or embedded car sensors. An example of a visual method is the study of [PERSON] et al., which calculated head yaw from the distortion of the isosceles triangle made by the mouth and the two eyes [28]. They make use of the facial feature point locations and head shape to estimate the head pose. Another work proposed geometric models that only used the location of the center of the face and face boundaries for head yaw estimation [29]. The geometric methods for head yaw estimation are invariant to facial expressions, support large head rotations, and work with or without glasses. [PERSON] et al. proposed a similar approach for estimating and tracking the 3D pose of a face obtained from a single monocular camera [30]. The shape of the 3D face was estimated by the ellipse and its aspect ratio. The detected face ellipse was then tracked in subsequent frames, which allowed tracking the 3D face pose. The authors claimed that their approach was more robust than existing feature-based approaches based on synthetic and real datasets. [PERSON] et al. proposed a system for estimating a head pose based on multi-view face detectors using a Naive Bayesian classifier [31]. The temporal variation of the head pose was modeled by HMM that predicted the optimal head pose. [PERSON] et al. developed a driver's facial pose recognition system that alarms if the face was distracted [32]. [PERSON] utilized the mouth and eye corner points for vanishing point and head pose estimation [33]. The authors reported the mean error in head pose estimation of 2.56\({}^{\circ}\), 1.67\({}^{\circ}\), and 3.54\({}^{\circ}\) around the X-axis, Y-axis, and Z-axis, respectively, over eight video sequences. [PERSON] et al. proposed a framework called Biased-Manifold Embedding for obtaining performance improvement in head pose estimation [34]. The experimental results obtained an average pose angle estimation error up to 2\({}^{\circ}\) on the FacePix dataset, which contained 5430 face images with pose variations at a granularity of 1\({}^{\circ}\). [PERSON] et al. developed an approach that combined non-linear techniques of dimensionality reduction with a learned distance metric transformation [35]. The experimental results showed that their method achieved accuracy in the range of 97% to 98% for facial images with varying poses and 96-97% accuracy on images with both pose and illumination variations. [PERSON] et al. demonstrated that discriminating power can be sufficiently boosted by applying the local manner in sample space, feature space, and learning space via linear subspace learning [36]. Experiments demonstrated that the local approach had around a 20% estimation error in head pose and 30% error in pitch. [PERSON] et al. presented a probabilistic framework that integrated the three approaches: the user independence and relative precision of differential registration, the stability and automatic initialization of static head pose estimation, and bounded drift of frame tracking [37]. [PERSON] et al. proposed a novel regression method for learning the regression between pose angles and image features and noise removal and outlier detection from the training data [38]. Experiments on real data with outliers demonstrated an MAR of 9.1\({}^{\circ}\) in yaw estimation and 12.6\({}^{\circ}\) in pitch estimation. [PERSON] et al. proposed a method for improving the accuracy of head pose estimation [39]. The symmetry of the face image and the head pose resulted in the use of Gabor filters and Local Binary Pattern operators in one-dimension.