What is real-time face anonymization?

Definition

Real-time face anonymization is the process of automatically detecting, identifying, and immediately concealing facial biometric features in live video streams or recorded footage. The goal is to prevent identification of individuals while preserving scene context. Each video frame is processed within a time budget not exceeding the frame interval (Le2e≤1/FvideoL_{e2e} \le 1/F_{video}Le2e​≤1/Fvideo​), ensuring uninterrupted anonymization.

Standards and normative references

  • GDPR Recital 26 — anonymous information falls outside the scope of GDPR; anonymization is effective only when the person cannot be identified “directly or indirectly.”
  • ISO/IEC 20889:2018Privacy enhancing data de-identification terminology and classification of techniques: provides formal taxonomy for anonymization and pseudonymization.
  • ISO/IEC TR 29100:2011Privacy framework: defines design principles for privacy-preserving systems, including data minimization and protection of biometric identifiers.

Technical parameters

Parameter

Requirement / typical value

Meaning

Le2eL_{e2e}Le2e​ (latency)

≤ 33 ms (for 30 FPS)

ensures real-time operation

Recall (face)

≥ 0.98

minimizes missed detections

Precision

≥ 0.90

limits false positives

Stable FPS

≥ F_{video}

maintains stream continuity

IoU (Region of Interest)

0.5–0.75

optimal anonymization area

Common techniques

  1. Gaussian Blur — computationally efficient and visually neutral.
  2. Pixelation (Mosaic) — lowers resolution in the detected facial region.
  3. Face Replacement (GAN-based) — substitutes detected faces with synthetically generated ones.
  4. Color/Thermal Masking — reduces visibility of biometric traits under low-quality conditions.

Practical applications

  • Public surveillance and body-worn cameras.
  • Online streaming and live broadcasting.
  • Privacy protection in HR, education, healthcare, and transport video systems.

Expert notes

Face anonymization is irreversible—unlike masking, it prevents any recovery of original facial data. Proper implementation requires continuous monitoring of recall metrics and frame-rate stability checks under peak processing loads to ensure compliance with data protection standards.