  The authors present a novel face tracking approach where optical flow
information is incorporated into a modified version of the Viola Jones
detection algorithm. In the original algorithm, detection is static, as
information from previous frames is not considered. In addition, candidate
windows have to pass all stages of the classification cascade, otherwise they
are discarded as containing no face. In contrast, the proposed tracker
preserves information about the number of classification stages passed by each
window. Such information is used to build a likelihood map, which represents
the probability of having a face located at that position. Tracking
capabilities are provided by extrapolating the position of the likelihood map
to the next frame by optical flow computation. The proposed algorithm works in
real time on a standard laptop. The system is verified on the Boston Head
Tracking Database, showing that the proposed algorithm outperforms the standard
Viola Jones detector in terms of detection rate and stability of the output
bounding box, as well as including the capability to deal with occlusions. The
authors also evaluate two recently published face detectors based on
convolutional networks and deformable part models with their algorithm showing
a comparable accuracy at a fraction of the computation time.
