Active vs. Passive Liveness Detection: What Every Business Should Know

16, October 2020

Liveness Detection Insurances E-government (eID)

Deepfakes, masks and even cut-out photographs: there are multiple ways in which fraudsters can spoof customer onboarding and authentication procedures. Companies need to stay alert of these threats. Advanced biometric authentication and anti-spoofing solutions such as passive liveness detection can help businesses stay ahead of the game.

With the pervasiveness of online and mobile transactions, scams and other fraudulent activities have also become much more prevalent. It is simply a must for companies to invest in up-to-date security measures, and this is especially important when it comes to digitally verifying customer and user identities. In order to protect themselves against fraudsters, companies should implement stringent customer authentication procedures – both at the onboarding stage and beyond.

Detecting liveness

Facial biometric technologies such as liveness detection solutions can greatly help companies during the onboarding process. The key part of any such biometric authentication solution is that it uses computer vision and deep learning algorithms to detect “liveness” or “presence” in a person — something that goes way beyond the more commonly known concept of facial verification.

Whereas facial verification only establishes that the face  in front of the camera corresponds to another, potentially already enrolled face, liveness detection clarifies whether there is a real, live person present, or whether the data stems from an inanimate object — a “spoof” as it’s called.

According to university researcher Dorothy Denning, who coined the term “liveness”, the crucial insight regarding liveness detection is that since a user’s most important biometric data point — his or her face — cannot be kept secret, a biometric identity verification system cannot rely on secrecy, but must rely on a quality intrinsic to the user’s identity — being alive.

The difference between “active” and “passive” liveness detection

Software solutions that are currently in use rely on two different types of liveness detection: Active and passive. A solution is called “active”, if it requires the user to do something in order to prove that he or she is a live person. Usually a user would be required to either turn their head, nod, blink or follow a dot on the phone’s screen with their eyes. With the “passive” approach on the other hand, the user doesn’t have to do anything.  That ensures a more streamlined and hassle-free experience for the end-user.

The active approach has been shown to be fraught with difficulties though, and can easily be spoofed by fraudsters in a so-called “presentation attack”. Bad actors can easily trick the system by using a host of different gadgets or “artifacts”, some of which are quite low-tech.

For example, an active liveness detection system that requires users to blink can easily be spoofed by a person wearing a print-out photograph of the individual they are impersonating with a cut-out where the eyes would be. They essentially “wear” that photograph over their face, with their own eyes looking through the cut-out and blinking when required to. More sophisticated hackers have also found ways to overcome active liveness solutions using attack vectors such as deepfakes or video replays.

For more info on the differences between active and passive liveness, make sure you take a look at our liveness detection FAQs on the subject. Here you will find many of the most common questions about this technology answered here too.

Why passive liveness detection is the better anti-spoofing solution

In order to most effectively guard against these types of presentation or spoofing attacks, companies are increasingly relying on “passive” liveness detection software. With passive liveness detection, the user has to do nothing while the software is running in the background.

In fact, users — and potential fraudsters too — may often not even be aware that an identity verification check is taking place.

Four reasons why passive liveness detection is superior to active liveness:

  1. It closes security gaps in facial biometric systems
  2. It makes for a smoother process
  3. It is faster
  4. It lowers drop-out rates significantly

1. Passive Liveness Detection closes security gaps in facial biometric systems

Passive liveness detection technology runs in the background without users even realizing that it is occuring, so-called “security through obscurity”. It detects features of presentation attacks such as edges, texture and depth to clearly distinguish a live person’s face from an inanimate or spoofed face. It also cannot easily be tricked by animation software that mimics facial expressions, such as smiling or frowning. It can deal with attack vectors such as deepfakes, masks, dolls and so on.

2. It makes for a smoother process

As passive liveness detection is not based on user interaction, it provides a much smoother identity verification process. Using their smartphones, users take a picture of their ID document and in a second step, verify themselves by taking a selfie with their smartphone camera. They don’t need to nod, turn their head or blink (in the active style). This improves user experience significantly.

3. It is faster

The entire passive liveness detection process takes only a couple of seconds. No instructions or manuals to follow. In a process that users in general do not enjoy going through, this can save you significant trouble.

4. It lowers drop-out rates significantly

With active liveness detection requesting actions that can be misunderstood, hard to follow or just ignored – the process can be interrupted at various points. This results in frustrated users dropping out and thus not creating any revenue for the company. As the process with passive liveness detection is fast and simple, major stumbling blocks toward onboarding the user have been removed.

The benefits of passive liveness detection that accrue to companies are clear:

  • Higher sales conversion rates
  • Lower onboarding costs
  • Increased protection from fraudsters and other bad actors

Toward a frictionless user experience

PXL Vision’s passive liveness solution is optimised for conversion, which means significantly fewer dropouts. The seamless integration of PXL Vision’s technology with a cloud or on-premise solution guarantees a frictionless user experience. This translates directly into higher onboarding numbers.

PXL Vision’s software solution is independent of hardware — it can be used on a cheap smartphone as well as on a top-of-the-range device. Its software solution is highly secure, tested to the highest standards and market proven. On top of that, it is extremely fast and can complete the entire verification process within seconds. Contact us to learn how your business can lower its costs and improve its digital onboarding requirements with PXL Vision’s passive liveness detection solution.

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