Dual-camera 3D head tracking for clinical infant monitoring

This paper presents a new algorithm for dual-camera 3D head tracking, intended for clinical infant monitoring. The paper includes a brief motivation with reference to the state-of-the-art in face-related image analysis. The proposed algorithm uses a clipped-ellipsoid head model and 3D head pose recovery by joint alignment of paired templates based on dense-HOG features. In the algorithm, template pairs are dynamically extracted and a limited number of template pairs are stored and re-used for drift reduction. We report experimental results on real-life videos of infants in bed in a hospital, captured in visual light as well as near-infrared light. Results show consistently good tracking behavior. For challenging video sequences, the mean tracking error in terms of endocanthion location error relative to the innercanthal distance remains below 30%. This error has proven to be sufficiently low for 3D head tracking to support infant face analysis. For this reason, the proposed algorithm is used successfully in an infant monitoring system under development.