# Personal Privacy Protection via Irrelevant Faces Tracking and Pixelation in Video Live Streaming

arXiv: https://arxiv.org/abs/2101.01060 · HTML: https://mlsanity.com/paper/2101.01060/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 59 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Builds on (most cited first, 17 of 17)

- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [ECO: Efficient Convolution Operators for Tracking](https://mlsanity.com/paper/1611.09224/) (2016)
- [Beyond Short Snippets: Deep Networks for Video Classification](https://mlsanity.com/paper/1503.08909/) (2015)
- [MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking](https://mlsanity.com/paper/1504.01942/) (2015)
- [End-to-end representation learning for Correlation Filter based tracking](https://mlsanity.com/paper/1704.06036/) (2017)
- [POI: Multiple Object Tracking with High Performance Detection and Appearance Feature](https://mlsanity.com/paper/1610.06136/) (2016)
- [PyramidBox: A Context-assisted Single Shot Face Detector](https://mlsanity.com/paper/1803.07737/) (2018)
- [Learning to track for spatio-temporal action localization](https://mlsanity.com/paper/1506.01929/) (2015)
- [Fast and Robust Hand Tracking Using Detection-Guided Optimization](https://mlsanity.com/paper/1602.04124/) (2016)
- [ArtTrack: Articulated Multi-person Tracking in the Wild](https://mlsanity.com/paper/1612.01465/) (2016)
- [Supervised Transformer Network for Efficient Face Detection](https://mlsanity.com/paper/1607.05477/) (2016)
- [Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking](https://mlsanity.com/paper/1808.01562/) (2018)
- [Cascaded Continuous Regression for Real-time Incremental Face Tracking](https://mlsanity.com/paper/1608.01137/) (2016)
- [Empirical likelihood test for high-dimensional two-sample model](https://mlsanity.com/paper/1504.05690/) (2015)

## Built on by (most cited first, 17 of 17)

- [CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search](https://mlsanity.com/paper/2306.10008/) (2023)
- [IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization](https://mlsanity.com/paper/2406.10580/) (2024)
- [Pre-training-free Image Manipulation Localization through Non-Mutually Exclusive Contrastive Learning](https://mlsanity.com/paper/2309.14900/) (2023)
- [Disguise without Disruption: Utility-Preserving Face De-Identification](https://mlsanity.com/paper/2303.13269/) (2023)
- [A Review on Visual Privacy Preservation Techniques for Active and Assisted Living](https://mlsanity.com/paper/2112.09422/) (2021)
- [Seeing is not Believing: An Identity Hider for Human Vision Privacy Protection](https://mlsanity.com/paper/2307.00481/) (2023)
- [Beyond Visual Appearances: Privacy-sensitive Objects Identification via Hybrid Graph Reasoning](https://mlsanity.com/paper/2406.12736/) (2024)
- [iFADIT: Invertible Face Anonymization via Disentangled Identity Transform](https://mlsanity.com/paper/2501.04390/) (2025)
- [Enabling Privacy-Aware AI-Based Ergonomic Analysis](https://mlsanity.com/paper/2505.07306/) (2025)
- [PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flow](https://mlsanity.com/paper/2307.09146/) (2023)
- [SHAN: Object-Level Privacy Detection via Inference on Scene Heterogeneous Graph](https://mlsanity.com/paper/2403.09172/) (2024)
- [Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy Versus Performance](https://mlsanity.com/paper/2410.18717/) (2024)
- [PPEDCRF: Privacy-Preserving Enhanced Dynamic CRF for Location-Privacy Protection for Sequence Videos with Minimal Detection Degradation](https://mlsanity.com/paper/2603.01593/) (2026)
- [TSDCRF: Balancing Privacy and Multi-Object Tracking via Time-Series CRF and Normalized Control Penalty](https://mlsanity.com/paper/2603.13667/) (2026)
- [Face anonymization preserving facial expressions and photometric realism](https://mlsanity.com/paper/2603.17567/) (2026)
- [PPEDCRF: Dynamic-CRF-Guided Selective Perturbation for Background-Based Location Privacy in Video Sequences](https://mlsanity.com/paper/2604.17163/) (2026)
- [VPD-100K: Towards Generalizable and Fine-grained Visual Privacy Protection](https://mlsanity.com/paper/2605.10229/) (2026)

## Abstract (arXiv)

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.