From foot to head: Active face finding using deep Q-learning

In the existing work on active face detection and tracking, it is usually required that the face has to appear in the field-of-view. However, this may not be practical in some challenging scenarios. In this paper, we formulate the problem of active face finding as a Markov Decision Process and resort to the deep Q-learning to solve it in an end-to-end manner. Under the proposed framework, the agent is able to learn how to adjust the control parameters of a camera in order to find the face. Even if the captured image contains only some parts of the person, the PTZ camera can still adjust its pose until the face is found. Extensive experimental validations are performed to show the effectiveness of the developed system.