Head pose monitoring is an important task for driver assistance systems, since it is a key indicator for human attention and behavior. However, current head pose datasets either lack complexity or do not adequately represent the conditions that occur while driving. Therefore, we introduce DriveAHead, a novel dataset designed to develop and evaluate head pose monitoring algorithms in real driving conditions. We provide frame-by-frame head pose labels obtained from a motion-capture system, as well as annotations about occlusions of the driver’s face. To the best of our knowledge, DriveAHead is the largest publicly available driver head pose dataset, and also the only one that provides 2D and 3D data aligned at the pixel level using the Kinect v2. Existing performance metrics are based on the mean error without any consideration of the bias towards one position or another. Here, we suggest a new performance metric, named Balanced Mean Angular Error, that addresses the bias towards the forward looking position existing in driving datasets. Finally, we present the Head Pose Network, a deep learning model that achieves better performance than current state-of-the-art algorithms, and we analyze its performance when using our dataset.

@inproceedings{Schwarz2016,
author = {Anke Schwarz, Lin Zhuang and Rainer Stiefelhagen},
title = {{ HeHOP: Highly Efficient Head Orientation and Position Estimation }},
year = {2016},
booktitle = {WACV IEEE Workshop on Applications of Computer Vision},
month = {Mar.},
doi = {}
}

Continuous head pose estimation is an important visual component for human-computer interaction. However, an accurate and computationally efficient method to estimate the head orientation and position remains a challenging task in computer vision. We propose a Highly efficient Head Orientation and Position estimation (HeHOP) approach based on depth data which uses a stage-by-stage regression framework. At each stage, binary features are obtained from local areas of depth information. A global linear mapping is used to predict the head orientation and position update using the binary features. We evaluate our method on the BIWI dataset containing depth images labeled with head orientation and position. The results show that our approach is robust against occlusions and achieves state-of-the-art performance in terms of accuracy, has a low miss rate, and is several times faster than previous methods.

@inproceedings{Schwarz2015,
author = {Anke Schwarz, Esther-Sabrina Wacker, Manuel Martin, M Saquib Sarfraz and Rainer Stiefelhagen},
title = {{ 3D Facial Landmark Detection: How to Deal with Head Rotations? }},
year = {2015},
booktitle={German Conference on Pattern Recognition} ,
organization={Springer}
}

3D facial landmark detection is important for applications like facial expression analysis and head pose estimation. However, accurate estimation of facial landmarks in 3D with head rotations is still challenging due to perspective variations. Current state-of-the-art methods are based on random forests. These methods rely on a large amount of training data covering the whole range of head rotations. We present a method based on regression forests which can handle rotations even if they are not included in the training data. To achieve this, we modify both the weak predictors of the tree and the leaf node regressors to adapt to head rotations better. Our evaluation on two benchmark datasets, Bosphorus and FRGC v2, shows that our method outperforms state-of-the-art methods with respect to head rotations, if trained solely on frontal faces.