Face Authentication

Liveness detection is a technology used in the face authentication process, which helps identify if the customer’s biometric samples are real, assessing whether the customer completing the check is a real live person or a fake representation. This technological advancement is most known for preventing spoofing attacks and any other attempts to fake facial biometrics. 

Active liveness detection requires users to perform specific actions to prove they’re alive and completing the verification check in real-time. For example, this might be requiring the user to blink, move in a certain way, or make a specific facial expression that is difficult to spoof. 

In contrast, passive liveness checks don’t require any user actions. Instead, the software continuously runs in the background, performing checks (such as screening the user’s skin texture, depth, edges, etc.) without the user’s awareness, which means that the process for the end-user is smoother compared to the active liveness approach. However, some jurisdictions strictly require companies to implement active liveness checks in order to comply with different regulations, such as the EU’s Anti-Money Laundering Directives (AMLDs). 

Face authentication works by comparing the customer’s face to another reference, for example, a photo of the person’s government-issued ID document, in order to find a match and verify that they are who they claim to be. Very often, this process is based on two verification measures: document and selfie verification. 

In practice, the process consists of the following steps:

1. The online process uses AI to read and extract information from the customer’s ID document. 
2. This collected information is verified for authenticity. 
3. Facial recognition technology matches the customer’s face with the photo on their document. 

Face authentication is used for both the first stage of the account opening process and later in the customer cycle to maintain ongoing due diligence. For example, fintechs can use this method to register verified users and grant them access to their services during the onboarding process. The same goes for verifying and registering a new account on a crypto platform. In both cases, face authentication compares the customer’s live image to the reference image to ensure that they’re the legitimate account holder.

Reverification is the same authentication process but used later on, after onboarding the customer, when there’s a risk or a need to check their identity again and ensure that they’re genuine. Banks, online marketplaces and other industries use this method because it’s fast, user-friendly, and secure, not overburdening the user to complete the full KYC check again. 

Some use-case examples of when reverification is used include:

• Before high-value transactions. This helps verify the user’s identity again and prevent account takeover (ATO) fraud. 
• Before accepting users who haven’t logged in in a long time. This helps ensure that the user is the same person who created the account and used it before.
• Before changing important details, such as a phone number or address. Passwords are easily compromised, which means an extra check can prevent such breaches.
• If suspicious activity is detected. For example, a fraudulent IP address or if the user has been blocked on a marketplace for security reasons and wants to access their account. 

All this considered, reverification helps with monitoring user behavior and improving the platform’s overall fraud prevention system.