  This paper investigates long-term face tracking of a specific person given
his/her face image in a single frame as a query in a video stream. Through
taking advantage of pre-trained deep learning models on big data, a novel
system is developed for accurate video face tracking in the unconstrained
environments depicting various people and objects moving in and out of the
frame. In the proposed system, we present a detection-verification-tracking
method (dubbed as 'DVT') which accomplishes the long-term face tracking task
through the collaboration of face detection, face verification, and
(short-term) face tracking. An offline trained detector based on cascaded
convolutional neural networks localizes all faces appeared in the frames, and
an offline trained face verifier based on deep convolutional neural networks
and similarity metric learning decides if any face or which face corresponds to
the queried person. An online trained tracker follows the face from frame to
frame. When validated on a sitcom episode and a TV show, the DVT method
outperforms tracking-learning-detection (TLD) and face-TLD in terms of recall
and precision. The proposed system is also tested on many other types of videos
and shows very promising results.
