Face Comparison Solutions for Seamless Identity Verification

UAREAL Face Comparison Solutions for Seamless Identity Verification.

UAREAL Face Comparison API for Identity based payments, security based access/deny systems, onboarding and eKYC.

UAREAL Face Comparison DEMO

Ascertain face similarity with our UAREAL Face Comparison API. Receive a confidence score and threshold value for a decisive similarity judgment. By converting faces into templates and calculating Euclidean distance, our API delivers accurate results, transforming them into a confidence value for seamless Face Comparison

TRY DEMO

Valid photo conditions

Incorrect photo conditions

Image Upload Guidelines

Not less than 45 degrees face up and 30 degrees face down.
The yaw should be less than 45 degrees in either direction.
Use an image of a face with both eyes open and visible.
Use an image of a face that is not tightly cropped.
Avoid items that block the face, such as headbands and masks.
Maximum file size 5mb
String encode base64

Frequently Asked Questions

Face comparison technolgy involves analyzing facial features to determine the similarity or dissimilarity between two faces, often used for identity verification.

It works by extracting facial features from images, converting them into templates, and calculating the similarity using algorithms like Euclidean distance.

The confidence score represents the level of certainty that two faces belong to the same person. Higher scores indicate greater similarity.

Yes, face comparison is widely used for security, including identity verification, access control, and fraud prevention.

Factors include image quality, lighting conditions, and the sophistication of the face comparison algorithm.

Reputable face comparison systems prioritize security and privacy, often using encryption and adhering to data protection regulations.

Yes, many face comparison APIs are designed for easy integration into various applications and systems.

Face comparison focuses on determining similarity between two faces, while face recognition identifies and matches faces from a database.

Advanced face comparison algorithms are designed to handle variations in pose and angles to some extent, but optimal results are achieved with frontal images.

The threshold value determines the minimum confidence score required to consider two faces as a match. Adjusting the threshold can impact the balance between false positives and false negatives.