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Face Recognition

Face recognition systems automatically identify or authenticate individuals from facial images or video footage using neural networks or classical computer vision methods. Such systems are increasingly deployed in security, law enforcement, and identity verification contexts. However, they are vulnerable to both spoofing attacks (presentation attacks) and deep synthesis attacks like deepfakes.

Key challenges

Spoofing and presentation attacks: Simple attacks using printed photos, masks, or display-based replay can fool recognition systems.

Adversarial robustness: Subtle perturbations to images or deepfake manipulation can cause dramatic failure in state-of-the-art systems, with false acceptance rates exceeding 95%.

Fairness and demographic bias: Recognition systems exhibit higher error rates on certain demographic groups (darker skin tones, women, underrepresented ethnicities), limiting deployment fairness.

Cross-domain generalization: Models trained on one set of recording conditions fail on video from different lighting, pose, or resolution.

Detection and security

Because deepfakes are increasingly realistic, traditional liveness detection (determining whether a face is real or video-based) requires multi-modal approaches:

  • Frequency-domain analysis: Looking for compression artifacts from GAN synthesis
  • Temporal consistency checks: Verifying facial expressions, eye blinks, and micro-expressions are naturalistic
  • Audio-visual synchronization: Detecting lip-sync mismatches or timing inconsistencies
  • Challenge-response protocols: Asking subjects to follow head movements or perform actions

Key papers in this wiki