Looking at faces in a vehicle: A deep CNN based approach and evaluation
Kevan Yuen, Sujitha Martin, Mohan Manubhai Trivedi · 2016
The driver's face is key to a less intrusive method to monitoring the driver to derive information such as distraction, drowsiness, intent, and where they are looking. A vital step in extracting these higher level information is to find the driver's face and individual components such as eyes, nose and mouth, along with the direction they are facing towards. In the context of safety critical situation like driving, it is important that this vital step be robust otherwise the higher level information is not available or unreliable. The lighting condition of a driver's cabin varies greatly from dark due to driving under a bridge or in a parking structure to extremely bright on a sunny day. Various occlusions on the face may also occur due to hand activities such as drinking water or different gestures. This work introduces a system based on existing CNN structures with slight modifications to implement face detection, landmark localization, and landmark-based head pose estimation method which addresses the challenges found in the driver cabin. To handle these challenges, training samples are artificially augmented for the purpose of developing a system robust in the environment of a vehicle.