Analyzing the appearances of political figures in large-scale news archives is increasingly important with the growing availability of large-scale news archives and developments in computer vision. We present a deep learning-based method combining face detection, tracking, and classification, which is particularly unique because it does not require any re-training when targeting new individuals. Users can feed only a few images of target individuals to reliably detect, track, and classify them. Extensive validation of prominent political figures in two news archives spanning 10 to 20 years, one containing three U.S. cable news and the other including two major Japanese news programs, consistently shows high performance and flexibility of the proposed method. The codes are made readily available to the public.

The collection of facial action data is essential for the accurate evaluation of a patient’s condition in the intensive care unit, such as pain evaluation. An automatic face-tracking system is demanded to reduce the burden of data collection on the medical staff. However, many previous studies assume that the optimal trajectory of a robotic tracking system is reachable which is inapplicable for large-amplitude head motions. To tackle this problem, we propose a region-based face-tracking algorithm for large-amplitude head motion with a 7-DOF manipulator. A configuration-based optimization algorithm is proposed to trade-off between theoretical optimal pose and workspace constraints through the assignment of importance weights. To increase the probability of recapturing the face exceeding the reachable workspace of the manipulator, the camera is directed toward the center of the head, named the facial orientation center (FOC) constraint. Furthermore, a region-based tracking approach is designed to stabilize the manipulator for small amplitude head motions and smooth the tracking trajectory by adjusting the joint angle in the null space of the 7-DOF manipulator. Experimental results demonstrate the effectiveness of the proposed algorithm in tracking performance and finding an appropriate configuration for the unreachable theoretical optimal configuration. Moreover, the proposed algorithm with FOC constraint can successfully follow the head motion as losing 33.2% of the face during the tracking.