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).

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.

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.