The client needed identity verification for high-traffic environments (access control points, attendance tracking, and event entry) where manual checks such as ID cards and physical registers were too slow and too easy to defeat.
AI-Powered Real-Time Face Identification System for Contactless Identity Verification
A facial-embedding platform that verifies identity in real time, replacing manual ID checks with fast, contactless recognition.
Overview
- Industry: Security & Identity Verification
- Engagement: AI Computer Vision Proof of Concept
- Focus: Facial Recognition, Real-Time Identity Matching
Objectives
- Replace slow, manual identity checks with automated, contactless facial verification
- Achieve high identification accuracy while keeping false-match rates low enough for operational use
- Support high-traffic environments where both speed and contactless verification matter
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Customer
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Business Challenge
Traditional identity verification is time-consuming, error-prone at volume, and vulnerable to impersonation. The organization needed an automated, contactless approach that could confirm identity in real time without adding friction for the people being verified.
01
Slow Manual Checks: ID cards and physical registers do not scale to high-traffic entry points.
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Impersonation Risk: Manual and document-based checks are vulnerable to misuse.
03
No Digital Trail: Paper-based and manual processes leave no reliable digital log for later review.
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Solution
MoreYeahs built a camera-based face identification system that captures live footage, detects faces, and generates a unique facial embedding for each one using deep learning models. Each embedding is compared against a reference database using similarity metrics, and the system assigns an identity match only when similarity clears a defined threshold, otherwise the face is logged as unknown.
Real-Time Detection & Alignment: Faces are detected, aligned, and normalized before embedding generation, improving match consistency across pose and angle variation.
Embedding-Based Matching: Deep learning embeddings are compared against a reference database using similarity metrics rather than brittle rule-based matching.
Unknown-Person Flagging: Faces that don't clear the similarity threshold are labeled unknown rather than forced into an incorrect match.
Full Identification Logs: Every match, or non-match, is logged with a timestamp and camera ID.
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Technology
The platform pairs a real-time computer vision pipeline with an embedding-based matching backend.
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Results
As a proof of concept, the system was tested against a defined set of accuracy and speed benchmarks before being considered for wider rollout.
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High Identification Accuracy: Face detection and identification accuracy targeted above 98% and 95% respectively on enrolled users during testing.
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Low Error Rates: False acceptance and false rejection rates targeted at or below 3% and 5%.
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Sub-Second Matching: End-to-end identification latency targeted at 1 second or less, supporting real-time use at entry points.
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Business Impact
Moving identity checks from paper and manual review to automated facial verification changes the experience on both sides of the counter: faster for the person being verified, and better logged for the organization doing the verifying.
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Contactless & Fast: Verification happens in real time without physical documents or manual review.
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Digital Audit Trail: Every identification event is logged, giving administrators a reliable record that manual registers never provided.
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Scalable to New Use Cases: The same embedding-and-matching architecture can extend to attendance, access control, or event management without rebuilding the core pipeline.
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