On looking at faces in an automobile: Issues, algorithms and evaluation on naturalistic driving dataset

Face detection is a vital step in the process of extracting semantic information about the driver's state, such as distraction and fatigue, from pixel values in images looking at the driver. Therefore, in the context of time and safety critical situation like driving, efficient use of time and reliable detection of faces is essential. While challenges like lighting and occlusion are prevalent in the vehicle cockpit and disruptive for time and reliabilities sake, the automobile cabin has a unique and advantageous environment for face detection. In this study we introduce a deep CNN based face detection method with discrete head pose estimation which address key challenges such as lighting conditions, occlusions, varying view points. One of the vital points in training the CNN based system is the compilation of positive samples via real-world dataset and synthetic data augmentation useful for in-vehicular settings. Performance evaluation on publicly available naturalistic driving data set, called VIVA-Face Dataset, shows promising results compared to baseline methods.