FaceR

Secure Face Recognition for Authentication

Security Status

System: Initializing...

Mode: Recognition (Strict)

Registered Faces (0)

No faces registered yet

Detected Faces

No faces detected

Security Features

Liveness Detection

Requires users to blink during registration to prove they're a live person, preventing photo/video spoofing attacks.

Face Quality Checks (Adaptive)

Registration Mode: More lenient thresholds to allow various angles and distances. Authentication Mode: Strict thresholds for security.

  • Brightness: Ensures proper lighting conditions
  • Blur Detection: Rejects blurry images using Laplacian variance
  • Frontal Pose: Accepts up to 35° angle during registration, 20° during auth
  • Face Size: Accepts 10% frame size (registration) or 15% (auth)

Pose & Distance Invariance

Face landmarks are normalized using inter-eye distance for scale and position invariance. This allows recognition to work across different distances and slight angle variations without compromising security.

Multi-Frame Validation

Captures 5 diverse frames during registration and requires consistent matching across frames, significantly reducing false positives.

Face Embeddings

Uses normalized feature embeddings with cosine similarity matching. Much more robust than simple landmark distance. Threshold set to 88% (balanced for angle/distance tolerance while maintaining security).

Anti-Spoofing Measures

  • Blink detection prevents static photo attacks
  • Quality checks reject printed photos (low quality)
  • Multi-frame diversity prevents video replay
  • Similarity threshold (88%) balances security with usability

Production Recommendations

  • Use dedicated models: Replace landmark-based embeddings with FaceNet/ArcFace ONNX models for production
  • Backend verification: For high-security applications, perform verification on a secure backend server
  • Audit logging: Log all authentication attempts for security monitoring
  • Encrypted storage: Store face embeddings encrypted in a secure database
  • Advanced anti-spoofing: Consider 3D depth sensors or specialized anti-spoofing models for critical applications
  • Multi-factor auth: Combine face recognition with other authentication factors (PIN, password, etc.)

Comparison

❌ Basic Implementation

  • Simple landmark distance
  • No liveness detection
  • No quality checks
  • 85% threshold
  • Single frame validation

✅ Secure Implementation

  • Normalized embeddings
  • Blink detection
  • 4 adaptive quality checks
  • 88% threshold (balanced)
  • 5-frame validation
  • Pose/distance invariance

Use Cases

  • Office Access: Employee authentication for building entry
  • App Login: Secure mobile/web app authentication
  • Payment Auth: Transaction verification
  • Identity Verification: KYC/onboarding processes