Deep Head Pose Estimation from Depth Data for In-car Automotive Applications
This addresses head pose estimation for in-car automotive applications, but it is incremental as it adapts existing deep learning methods to depth data.
The paper tackles head pose estimation using a Convolutional Neural Network that works directly on raw depth data, achieving state-of-the-art results on the Biwi Kinect Head Pose dataset with real-time performance.
Recently, deep learning approaches have achieved promising results in various fields of computer vision. In this paper, we tackle the problem of head pose estimation through a Convolutional Neural Network (CNN). Differently from other proposals in the literature, the described system is able to work directly and based only on raw depth data. Moreover, the head pose estimation is solved as a regression problem and does not rely on visual facial features like facial landmarks. We tested our system on a well known public dataset, Biwi Kinect Head Pose, showing that our approach achieves state-of-art results and is able to meet real time performance requirements.