Liveness detection is integral to enhancing security and verifying identities in sectors like banking, mobile payments, and access controls.

In today's digital security landscape, marked by a spike in identity fraud — with losses reaching $43.3 billion in the U.S. in 2022 — this technology is key for effective user authentication.

It combats fraud and identity theft by differentiating real individuals from false representations, such as photos, videos, or masks. This article delves into various liveness detection methods and introduces our novel anti-spoofing solution to this critical issue.

Methods of liveness detection

Passive Liveness Checks

Passive face liveness detection analyzes facial imagery in videos or photos, focusing on lighting and texture. This process is crucial for anti-spoofing, ensuring that the image is of a real person and not a fake.

Below is a practical example demonstrating how our system effectively discerns between genuine and fraudulent images:

Active Liveness Checks

The method relies on the individual undergoing biometric authentication to perform specific actions such as blinking, smiling, raising eyebrows, nodding, slow blinking, nodding up and down, turning their head left and right, as well as bringing their hand to their face and rotating it around its own axis.

By capturing and analyzing these movements, the liveness detection API ensures reliable and accurate authentication.

This approach offers a high level of security, making it difficult to counterfeit, and provides robust protection in fraud prevention against unauthorized access, crucial for any application requiring facial-based authentication.

Hybrid Liveness Method

Combining both passive and active checks, this method ensures quick verification, shifting to active liveness if passive data is insufficient​​.

Learn more about other techniques and how Luxand.cloud can help you and your business here: Enhance Security and Defeat Spoofing: A Comprehensive Guide to Liveness Detection Implementation