  To date, the privacy-protection intended pixelation tasks are still
labor-intensive and yet to be studied. With the prevailing of video live
streaming, establishing an online face pixelation mechanism during streaming is
an urgency. In this paper, we develop a new method called Face Pixelation in
Video Live Streaming (FPVLS) to generate automatic personal privacy filtering
during unconstrained streaming activities. Simply applying multi-face trackers
will encounter problems in target drifting, computing efficiency, and
over-pixelation. Therefore, for fast and accurate pixelation of irrelevant
people's faces, FPVLS is organized in a frame-to-video structure of two core
stages. On individual frames, FPVLS utilizes image-based face detection and
embedding networks to yield face vectors. In the raw trajectories generation
stage, the proposed Positioned Incremental Affinity Propagation (PIAP)
clustering algorithm leverages face vectors and positioned information to
quickly associate the same person's faces across frames. Such frame-wise
accumulated raw trajectories are likely to be intermittent and unreliable on
video level. Hence, we further introduce the trajectory refinement stage that
merges a proposal network with the two-sample test based on the Empirical
Likelihood Ratio (ELR) statistic to refine the raw trajectories. A Gaussian
filter is laid on the refined trajectories for final pixelation. On the video
live streaming dataset we collected, FPVLS obtains satisfying accuracy,
real-time efficiency, and contains the over-pixelation problems.
