Facial Recognition
Powered by in-house-developed technology, our facial recognition solution
Industry leaders trust Incode with their AI fraud prevention
How it works
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.
Face detection
Identifies, isolates, and analyzes unique facial features from an image or video for subsequent analysis, often within milliseconds.
Feature extraction
Analyzes and identifies unique features, facial patterns, and characteristics, ensuring accurate recognition, no matter the expression or lighting.
Vector conversion
Converts extracted features into a numeric representation of the facial biometrics. This becomes a unique “facial signature.”
Encryption for security
Securely encrypts the vector into a format that can only be opened and interpreted with the correct decryption key.
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).
Our technology
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.
Demographic fairness
Unbiased performance
across demographics.
Environmental adaptability
Reliable results in any condition or environment.
Accuracy at scale
Exceptionally low (0.01%) occurrence
of false matches.
High efficiency
Facial matching completed
in 20 milliseconds.
Data integrity
Trained on millions of proprietary,
compliant images.
Unlock the power of facial recognition today
Achieve fast and frictionless verification with outstanding accuracy.
Premium performance
Trusted security, proven accuracy
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.
ISO (30107-3)
Certified against biometric spoofing and presentation attacks
100%
success rate in spotting and blocking
fraudulent selfies
99.9%
success rate in identifying and passing
genuine selfies
Verifications processed in 20 milliseconds
20 ms
Recognized as a top remote identity validation provider by the Department of Homeland Security
Trusted by the world’s leading companies
Enterprise-grade security and compliance
1:1 v 1:N
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.
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.
Resources
Latest insights on facial recognition from Incode
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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