Top Enterprise Facial Recognition

Certified Passive Liveness Detection

Computational Photography

Robust ID Validation

Incode is recognized as a leader in enterprise-grade facialrecognition by the US Government (NIST).

Incode has developed proprietary edge and server facial recognition. The technology runs efficiently on the edge onlow-end phones with poor-quality cameras and on server,while maitaining high accuracy levels.

Top EnterpriseFacial Recognition

Highly ranked by

NIST FRVT July 30, 2019 report

beamIncode’s LiveBeam hardware-less anti-spoofing technology is the first and only iBeta certified liveness that doesn’t require user interaction, and 1 out of 2 technologies to have receivedthe iBeta facial liveness (PAD) certification in general.

Incode’s passive liveness solution useslight-modeling to perform liveness with just one photo frame of a user and with banking level security. This helps differentiate between a real person and a photo or video of that person.

Users don’t need to perform awkward active liveness actions (e.g. blinking, raising eyebrows, keystroking). The solution works on low-end and high-end devices both native and on web and doesn’t require special hardware.

Certified by

Certified PassiveLiveness Detection

Incode’s ID validation models leave no marginfor fraud.

Incode’s ID detection runs key tests on the IDs tonet any kind of identity theft attempts. Fake check, tamper check, and liveness check are some of the basic deep learning-base validations that run onthe ID to detect fraud.

Incode supports a global database of 6,000+government-issued IDs in over 190 countries.

Incode’s facial recognition technology andencryption systems are designed to be secureand convenient in every platform.

Robust ID Validation

Edge computational photography is thescience of capturing useful frames from users.

100+ computational adjustments areperformed to the photo on device and server to carefully guide the user to take a functional photo and perform photographic adjustments (e.g. brightness, contrast, rotation).

These user instructions and automaticadjustments ensure a smooth customerexperience and a better recognition capture.

Computational Photography