ID Tech

ABIS & Deduplication

Automated Biometric Identification Systems perform large-scale 1:N searches to resolve identities and detect duplicates.

Overview

ABIS platforms store and search biometric templates at national or global scale. Deduplication ensures each person has only one identity record.

How it works

  1. Capture biometric samples during enrollment.
  2. Extract features and store templates.
  3. Run 1:N searches to detect matches or duplicates.
  4. Adjudicate hits and maintain watchlists.

Common use cases

  • National ID enrollment
  • Border and visa vetting
  • Civil or criminal watchlists

Strengths and limitations

Strengths: Scales to millions; prevents multiple identities.
Limitations: Infrastructure cost; privacy and governance.

Key terms

  • 1:N search: Matching a probe against all records.
  • Deduplication: Removing duplicate identities in a database.

References

Vendors using ABIS & Deduplication

Thales GroupLakota Software Solutions

Latest Data Cards

  • Data Card

    Peel Regional Police Issues RFP for Cloud-Based AFIS Integrated with RCMP RTID

    2025-12-22CC-BY-4.0fingerprint-recognitionabis-dedup

    Peel Regional Police issued an RFP for a cloud-based automated fingerprint identification system (AFIS) integrated with the RCMP’s real-time identification capability.

    • The tender describes a 60-month procurement for supply, implementation, and support of an AFIS-RTID solution.
    • The listing describes a questions deadline of January 20, 2026 and a bid closing date of January 28, 2026.
    • The listing identifies several plan takers, including Canadian entities associated with large identity and biometrics vendors.
See more data cards

Frequently Asked Questions

What does an ABIS do?
It searches biometric databases to identify individuals and flag duplicate enrollments across large populations.
What’s the difference between identification and verification?
Identification is 1:N (who is this?); verification is 1:1 (is this person who they claim?). ABIS primarily supports 1:N at scale.
How are false positives managed?
Systems tune thresholds and use adjudication workflows; quality checks and multi‑modal fusion reduce spurious hits.