Facial Recognition

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

Industry leaders trust Incode with their AI fraud prevention

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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](/content/technology/face-recognition#fd/index.html)
Feature extraction](/content/technology/face-recognition#fe/index.html) .svg)
Vector conversion](/content/technology/face-recognition#vc/index.html) .svg)
Encryption for security](/content/technology/face-recognition#efs/index.html) .svg)
Comparison for verification](/content/technology/face-recognition#cfv/index.html)

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.

Unlock the power of facial recognition today

Achieve fast and frictionless verification with outstanding accuracy.

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.

100%

success rate in spotting and blocking fraudulent selfies

20 ms

Verifications processed in 20 milliseconds

ISO (30107-3)

Certified against biometric spoofing and presentation attacks

99.9%

success rate in identifying and passing genuine selfies

Frequently Asked Questions

What is face recognition software?

Face recognition software identifies or verifies a person's identity by analyzing their facial features and comparing them against a reference image — such as a selfie matched to a passport photo — using AI and biometric algorithms.

How accurate is facial recognition software?

Incode's facial recognition is NIST-certified, verifying identities in 20ms with industry-leading accuracy across all demographics. It ranks consistently in the top tier of NIST FRTE benchmarks.

How does facial recognition differ from face detection?

Face detection locates and identifies the presence of a face in an image. Face recognition goes further — it analyzes and compares facial geometry to verify or identify the specific individual, typically using a biometric template.

Is facial recognition software compliant with GDPR and CCPA?

Yes, when implemented correctly. Compliant facial recognition systems obtain explicit consent, store only encrypted biometric templates (not raw images), and provide users with data deletion rights. Incode's platform is built privacy-first with full compliance support.

What is the role of liveness detection in facial recognition?

Liveness detection ensures the face being scanned belongs to a physically present, live person — not a printed photo, video replay, or deepfake. It's a required layer in any biometric system used for identity verification or authentication.