Face Tracking by Means of Continuous Detection

The main contribution of this work is a new view to face-tracking namely to associate the independent detection results obtained by applying a real-time detector to each frame of a sequence. This differs fundamentally from traditional tracking which is mostly understood as finding the location of a target object given its previous position and a correspondence finding algorithm either with or without a prior object model. This traditional notion has two major problems namely the initialization problem and the lost-track problem. We show that the advent of new rapid detection algorithms may change the need for traditional tracking. Furthermore the mentioned problems have a natural solution within the presented tracking by continuous detection approach. The only assumption on the object to track is it\´s maximal speed in the image plane, which can be set very generously. From this assumption we derive three conditions for a valid state sequence in time. To estimate the optimal state of a tracked face from the detection results a Kalman filter is used. This leads to an instant smoothing of the face trajectory. It can be shown experimentally that smoothing the face trajectories leads to a significant reduction of false detections compared to the static detector without the presented tracking extension. We further show how to exploit the highly redundant information in a natural video sequence to speed-up the execution of the static detector by a temporal scanning procedure which we call "slicing". A demo program showing the outcomes of our work can be found in the internet under http://www.iis.fraunhofer.de/bv/biometrie/ for download.