Facial Recognition

Powered by in-house-developed technology, our facial recognition solution delivers unmatched accuracy, speed, and fairness, proven in real-world environments.

An image of a man's face displayed on a mobile device, with a blue checkmark indicating identity verification.
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CITI logo.
Chime logo.
Amazon logo.
TikTok logo.
BETMGM logo.
SIXT logo.
Fanduel logo.
Experian logo.
Rappi logo.
HSBC logo.
PayJoy logo.
Banorte logo.
Equifax logo.
AT&T logo.
H&R Block logo.
NU logo.
Scotiabank logo.
Progressive Leasing logo.
Wisconsin Department of Transportation seal logo.

Precision in every pixel

Our facial recognition technology uses advanced machine learning (ML) models to compare images with ID photos or previously captured pictures. This ensures accurate verification and fortifies fraud prevention, maintaining a seamless user experience.

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Face detection

Identifies, isolates, and analyzes unique facial features from an image or video for subsequent analysis, often within milliseconds.

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Feature extraction

Analyzes and identifies unique features, facial patterns, and characteristics, ensuring accurate recognition, no matter the expression or lighting.

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Vector conversion

Converts extracted features into a numeric representation of the facial biometrics. This becomes a unique “facial signature.”

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Encryption for security

Securely encrypts the vector into a format that can only be opened and interpreted with the correct decryption key.

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Comparison for verification

Compares face templates extracted from a selfie or an ID image against another template (1:1) or against a database of templates (1:N).

The gold standard for facial recognition

Our pioneering technology is powered by globally inclusive and diverse training data, resulting in high recognition accuracy regardless of ethnicities, age, gender, or environmental conditions.

Unlock the power of facial recognition today

Achieve fast and frictionless verification with outstanding accuracy.

Trusted security, proven accuracy

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Incode’s facial recognition models are NIST-certified and top ranked in FRTE benchmarks for 1:1 verification and 1:N identification, tested on millions of images for accuracy and fraud detection.

100%

success rate in spotting and blocking fraudulent selfies

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20 ms

Verifications processed in 20 milliseconds

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ISO (30107-3)

Certified against biometric spoofing and presentation attacks

99.9%

success rate in identifying and passinggenuine selfies

Recognized as a top remote identity validation provider by the Department of Homeland Security

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Really good technology, probably the best ML models on the market.
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Trusted by the world’s leading companies
Enterprise-grade security and compliance

Face recognition use-cases

1:1 verification

What it is: A selfie is compared to a single reference photo (e.g., from a government-issued ID) to confirm that both belong to the same person.

How it might be used: When a user opens a new bank account online, Incode compares their selfie to the photo on their government-issued ID. This proves ownership of the ID by the user, preventing impersonation and meeting compliance requirements.

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1:N identification

What it is: A single face image is compared against a database of many enrolled profiles to find a match or confirm that no match exists.

How it might be used: A financial institution checks a new customer’s selfie against its database of existing clients to ensure the person is not already enrolled under another identity. This prevents duplicate accounts, fraud, and compliance violations.

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Latest insights on facial recognition from Incode

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Get ahead of the facial recognition curve

Personalize and simplify your services with accurate facial recognition, built on Incode’s advanced ML models.

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