# Pixelation is NOT Done in Videos Yet

## TL;DR

This paper presents a novel face blurring algorithm for streaming videos that effectively prevents face recognition, using clustering and Gaussian Processes to improve trajectory accuracy, and evaluates user perception through online experiments.

## Contribution

It introduces a new clustering algorithm and Gaussian Process refinement for face trajectory creation in privacy-preserving video blurring.

## Key findings

- Face blurring effectively prevents recognition.
- Participants preferred blurred videos over original.
- User satisfaction and social presence are maintained.

## Abstract

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)