Skip to main content
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. The Academy lesson on reading a verification result reaches face search and block lists at 6:53.
Didit face search 1:N flow integrated with liveness for duplicate and blocklist detection

​
Automatic Face Search Integration

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

​
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

​
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

​
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

​
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: Matching uses numeric face templates scoped to your application; the index never leaves your tenant

​
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.

​
How It Works

Face Extraction

When a search is initiated, the system processes the reference image:

Comparison Algorithm

The system searches across your entire database of verified sessions:
  • Compares the reference facial vector against every face enrolled in your application: session faces, faces uploaded to User profiles, your face lists, and any retained biometric templates
  • 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: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

​
Deleted sessions and retained biometric templates

Deleting a session deletes its face embedding by default, so the person drops out of the index and can verify again without a duplicate flag. Applications that delete sessions soon after approval but still need to catch repeat sign-ups can enable biometric-template retention. Didit then keeps one image-free biometric template anchored to the User after the session is deleted, and that template takes part in Face Search and automatic duplicate detection exactly like a live session face. A hit on a retained template is reported with source: "retained_template", the owning User’s vendor_user_id and vendor_data, and a biometric_template_id. It carries no session id, match image, identity details, or session status, because the session that produced the face no longer exists. Retained templates are never blocklisted; face blocklist entries keep their own independent biometric entry. The template stays until its scheduled expiry, User deletion, a privacy-erasure request, or an explicit purge through the Biometric Templates API or Lists → Biometric templates in the Console.

​
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.

​
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