  This paper introduces an algorithm to protect the privacy of individuals in
streaming video data by blurring faces such that face cannot be reliably
recognized. This thwarts any possible face recognition, but because all facial
details are obscured, the result is of limited use. We propose a new clustering
algorithm to create raw trajectories for detected faces. Associating faces
across frames to form trajectories, it auto-generates cluster number and
discovers new clusters through deep feature and position aggregated affinities.
We introduce a Gaussian Process to refine the raw trajectories. We conducted an
online experiment with 47 participants to evaluate the effectiveness of face
blurring compared to the original photo (as-is), and users' experience
(satisfaction, information sufficiency, enjoyment, social presence, and filter
likeability)
