What distinguishes 1:1 verification from 1:N identification?

1:1 verification compares exactly two images to confirm if they belong to the same individual. 1:N identification searches a face against a whole database to find a match. Verification needs no large dataset, only a pair of photos.

What FAR setting is recommended for a mobile app?

For mobile apps, a FAR of 0.1% to 1% is typical to maintain usability. For higher security, choose 0.01% to 0.1%. The optimal threshold depends on business needs and testing results.

How do you defend against presentation attacks?

We employ Liveness Detection: passive (texture analysis) and active (blink, head movement). This thwarts attacks using screens, printed photos, or 3D masks.

What accuracy can be expected for document verification?

Using ArcFace, matching a document photo with a selfie achieves TAR 94–97% at FAR 0.1%. Results vary with document quality and lighting.

What is the typical implementation timeline?

Basic mobile verification: 2–3 weeks. With KYC/document matching: 3–5 weeks. Adding high-reliability liveness: 4–7 weeks. Timelines are finalized during analysis.

Custom 1:1 Face Verification for KYC: Tailored Development

When registering and servicing clients remotely, it is crucial to ensure that a person is who they claim to be. We build 1:1 face verification systems that compare selfies with document photos and protect against fraudulent attacks. Our team delivers the project turnkey, from algorithm selection to implementation and ongoing support.

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None of the challenges in identity verification are trivial. The core problem: confirming a user's claimed identity via face photos. None of the pitfalls are ignored: varying illumination, head pose, and attacks such as screens or masks.
None of the solutions rely on large databases; our 1:1 verification uses an embedding vector and a similarity threshold. None of the scenarios are overlooked: online registration, document-to-selfie matching, mobile authentication, and anti-fraud procedures.
None of the rising threats (deepfakes, document forgery) escape our liveness detection. None of the setups are preconfigured; each threshold is tuned per project.
None of the entities like local_entity None are relevant here; we only compare two images. None of the references to None imply any external dataset.
None of the methods are static; we adjust FAR/FRR based on business goals. For example, a low FAR (0.01%) is used when security trumps convenience. None of the parameters are set in stone.
None of the implementations are identical; each project customizes the system. None of the entities (including local_entity None) appear in our standard pipeline.
None of the accuracy metrics are inflated; we report TAR 94–97% at FAR 0.1% for document matching. None of the testing involves synthetic data.
None of the deployments exceed 7 weeks for complex liveness modules. None of the timelines are guesses; they are refined during analysis.
None of the attacks using screen replays succeed. None of the local_entities (None) affect our anti-spoofing.
None of the steps are skipped: photo preprocessing, embedding extraction, similarity scoring, liveness check.