  Most current multi-object trackers focus on short-term tracking, and are
based on deep and complex systems that do not operate in real-time, often
making them impractical for video-surveillance. In this paper, we present a
long-term multi-face tracking architecture conceived for working in crowded
contexts, particularly unconstrained in terms of movement and occlusions, and
where the face is often the only visible part of the person. Our system
benefits from advances in the fields of face detection and face recognition to
achieve long-term tracking. It follows a tracking-by-detection approach,
combining a fast short-term visual tracker with a novel online tracklet
reconnection strategy grounded on face verification. Additionally, a correction
module is included to correct past track assignments with no extra
computational cost. We present a series of experiments introducing novel,
specialized metrics for the evaluation of long-term tracking capabilities and a
video dataset that we publicly release. Findings demonstrate that, in this
context, our approach allows to obtain up to 50% longer tracks than
state-of-the-art deep learning trackers.
