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Face Search is a powerful feature that allows you to search for a specific face across all your approved identity verification sessions. This capability helps identify duplicate accounts, prevent fraud, and enhance your security measures.

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Automatic Face Search Integration

Face Search is automatically performed during liveness checks in verification sessions to detect duplicate users and check against blocklisted faces.

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Automatic Duplicate Detection

When a user completes a liveness check during identity verification:
  • Their facial biometrics are automatically compared against all previously verified users
  • The system identifies potential duplicate accounts based on facial similarity
  • Matches are flagged according to your configured similarity thresholds
  • You can review and take action on potential duplicate users

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Blocklist Integration

Face Search seamlessly integrates with the blocklist feature:
  • During verification, faces are automatically checked against your blocklist
  • If a match to a blocklisted face is found, the verification is automatically declined
  • This prevents previously identified problematic users from creating new accounts
  • Helps maintain the integrity of your verification process

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API Access

Face Search functionality is also available through our API, allowing you to:
  • Programmatically submit face searches
  • Integrate face matching capabilities into your own applications
  • Build custom fraud detection workflows
  • Create automated systems for duplicate detection

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Key Features

  • High Accuracy: Advanced biometric algorithms provide reliable match results
  • Configurable Thresholds: Customize match sensitivity based on your risk tolerance
  • Comprehensive Scanning: Search across all your verified users
  • Rapid Results: Process searches quickly even with large user databases
  • Privacy-Focused: All processing happens within your secure environment

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Configurable Thresholds

You can customize search sensitivity by setting different thresholds for similarity scores:
These thresholds can be adjusted based on your risk tolerance and security requirements.

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How It Works

Face Extraction

When a search is initiated, the system processes the reference image:
ProcessDescription
Feature extractionIsolates facial features from the reference image
NormalizationStandardizes facial data for consistent comparison
Quality validationChecks image quality and facial clarity
Vector encodingCreates a mathematical vector representation of the face

Comparison Algorithm

The system searches across your entire database of verified sessions:
  • Compares the reference facial vector against all stored vectors
  • Employs advanced neural network architecture optimized for speed and accuracy
  • Supports two search modes: most similar (ranked list) and blocklisted or approved (status-filtered)
  • Processes large databases rapidly using optimized indexing

Similarity Scoring

For each comparison, a similarity percentage is generated:
Score RangeInterpretation
90%+Strong match — very likely the same person
70–89%Possible match — may require manual review
Below 70%Likely different individuals
Your configured match thresholds determine which results are flagged.

Results Delivery

The system returns a comprehensive result set:
  • Ranked list of potential matches sorted by similarity score
  • Match details including session ID, verification date, and vendor data
  • Similarity percentage for each match
  • Match images available for visual review
  • Blocklist status indicating if the matched face is blocklisted

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Similarity Percentage

The similarity percentage is the core metric used to determine potential matches:
  • High percentage (typically 90% and above): Indicates a strong likelihood that the faces belong to the same person.
  • Medium percentage (70-89%): Suggests possible matches that may require further review.
  • Low percentage (below 70%): Likely indicates different individuals.
The exact threshold for what constitutes a “match” can be configured based on your security requirements. Increasing the threshold reduces false positives but may increase false negatives.

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Use Cases

  • Fraud Prevention: Identify users attempting to create multiple accounts
  • Enhanced KYC: Add an additional layer of verification to your KYC process
  • Regulatory Compliance: Meet requirements for detecting duplicate accounts
  • Access Control: Verify user authenticity for high-security areas
  • Law Enforcement: Assist authorized agencies in identifying persons of interest