How Active Liveliness Is Different From Passive Liveliness

What Is Liveness Detection?

Face recognition is being frequently chosen by many enterprises as a reliable, solid, hassle-free process of onboarding, validating, and approving customers. Liveness detection has grown more prominent at a rapid rate. Liveness detection is a method of identifying presentation attacks like photo or video spoofing, deepfakes, models, or 3D masks, preferably than matching the facial features, which scammers can easily trick.

Why Business Demand Liveness Detection

With the pervasiveness of online and portable transactions, scams and additional fraudulent activities have also grown much more widespread. There are various techniques in which fraudsters can fool customer onboarding and authentication styles. Corporations lack to stay observant of certain threats. It is naturally necessary for businesses to spend on up-to-date safeguard standards, particularly when verifying customer and user identifications digitally. Excellent biometric authentication and anti-spoofing solutions such as Valydate4u can benefit companies to lead the game.

How Active Liveliness Is Different From Passive Liveliness

Active Liveness Detection: A technique is named “active” if it expects the user to do something to confirm that they are a live person. Normally, a user would be expected to either change the head position, nod, blink their eyes or follow a mark on their device’s screen with their eyes. Nevertheless, the active method is fraught with challenges and can be fooled by fraudsters in a so-called presentation attack known as the PAD attack. Scammers can cheat the system by practicing a host of several gadgets or “artifacts,” some of which are considerably low-tech.

Passive Liveness Detection: Conversely, with the “passive” method, the user is not asked to do anything. That secures a more modernized and hassle-free practice for the end-users. It is an excellent method that recognizes whether a user is present without any specific movement or gesture. Passive methods typically take a single image, which is examined for an array of multiple characteristics to conclude if a live person is present or not.

How We Use Active Liveness & Passive Liveness For Enhanced Protection

In our Valydate4u solution- A complete customer onboarding platform, we have implemented active liveness and passive liveness, adopting computer vision and deep learning algorithms to distinguish “liveness” or “presence” of a person to push way behind the more regularly used concept of facial verification.

For Passive liveness detection, we practice Lambertian reflectance algorithms that examine data after obtaining information from biometric scanners and readers to confirm if the source is coming from a fake image or not. Our single image passive liveness method has been rigorously examined on industry-standard benchmarks and has achieved more than 99% accurate predictions in real scenarios.

Our Active liveness detection methods operate in real-time and incorporate:

• Capturing the smile of the user.
• Examining a selfie by looking left and right side.
• Depth-sensing strategies like detecting hands and fingers.

Why Valydate4u?

When using face biometrics for onboarding, efficiency is a primary concern for many companies. Moreover, spoofing attacks like presentation attacks, picture attacks, and replay attacks are vital threats. Organizations involved in implementing financial duties such as lending, insurance, loans, wealth administration, mortgages, and others must guarantee they conduct individual KYC users before permitting access to services. Valydate4u requires no extra end-user effort, has the lowest drop-off rates, adds no latency to the end-user journey, inexpensive manual operational cost, very minimal turnaround time, and operates well in poor network conditions. All this comes for you with assurance against frauds