Liveness Detection

Fraudsters often try to pass the verification using various methods. Some are considered to be simple, such as paper masks and other head-only artifacts. Other, more skilled criminals use various techniques, such as Human-like silicone dolls, high-resolution 2D photos, or digital technology, such as deepfakes and other renderings that artificially change the skin color or add intense glare and shadow. 

Active liveness relies on active communication (or user involvement), which means that the user is asked to move their head, for example, while passive liveness doesn’t require extra steps from the user because the check runs in the background without disrupting the experience for the person. Both are considered to be secure liveness verification methods in identity verification and can detect spoofing by examining the selfie and the person’s skin texture and other biometric traits. 

Hybrid liveness is another category of liveness detection where semi-passive liveness checks are used, meaning that elements from both active and passive liveness make up the verification process. Here, the goal is to improve security and make the verification process more seamless, similar to how passive liveness checks work without disruption for the user. 

Liveness detection, especially passive liveness checks, requires minimal effort from the user and doesn’t challenge them to complete extra steps that might result in drop-offs. This is very important in a highly competitive market, for example, an e-commerce platform that sells age-restricted items and needs to conduct KYC checks as part of its age verification requirements. Since some users might be hesitant to provide personal information, businesses aim to create a secure and simple sense to improve the experience, and liveness is perfect for that. 

The biggest benefits that make companies decide to switch to a biometric verification process with liveness detection are improved user experience and ensured fraud prevention at the first stage of the user journey. Fraudsters are detected in real time before they are accepted into a platform. 

Liveness checks ensure that high-quality selfies are accepted and that images that don’t meet security standards are rejected. Since active liveness requires responding to specific actions, such as a head movement, fraudulent attempts become much harder. Additionally, such data analysis helps detect suspicious behavior, providing deeper fraud insights.