Active and Passive Liveness Detection

October 16, 2020

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Identity Verification Liveness Detection Digital Onboarding

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

From deep fakes, to masks and even cut-out photographs: there are multiple ways in which fraudsters can spoof online customer onboarding and authentication procedures. Fortunately, there are advanced biometric authentication and anti-spoofing solutions available on the market such as passive liveness detection which can help businesses stay ahead of the game.

With online and digital transactions ever-increasing, scams and other fraudulent activities are also becoming much more prevalent. It is now an absolute MUST for companies to invest in up-to-date security measures; all the more so 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.

What is liveness detection?

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, previously saved face - liveness detection answers the question of whether it is a real, live person present, or whether the data stems from an inanimate object — or a so-called “spoof”.

According to 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 identity verification solutions 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 living 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, the user doesn’t have to do anything except for sit still and wait for the process to finish. Thus, passive liveness detection ensures a much more streamlined and hassle-free experience for the end-user.

The active approach is fraught with issues 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, like 3D paper cut-out masks.

For example, an active liveness detection system which 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” the photograph over their face, with their own eyes looking through the cut-out and blink when required. More sophisticated hackers have also found ways to overcome active liveness solutions using attack vectors such as deepfakes or video replays.

For more information 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 commonly asked questions about this technology answered.

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 recognition systems
  2. It provides a smoother process
  3. It is much faster than active liveness detection
  4. It lowers drop-out rates significantly

1. Passive liveness detection closes security gaps in facial recognition systems

Passive liveness detection technology runs in the background sometimes without users even realizing that it is occurring, 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 also deal with so-called presentation attacks such as deepfakes, 3D 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 user experience (UX) for the identity verification process. Via their smartphones, users first take a picture of their ID document and in a second step, verify themselves by taking a short video selfie with their smartphone camera. They don’t need to nod, turn their head or blink (as with active liveness). This improves UX significantly.

3. It is much faster than active liveness detection

The entire passive liveness detection process takes about 10-15 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

Active liveness detection processes ask users to perform actions that can be misunderstood, hard to follow or just ignored. This results in frustrated users dropping out and thus potential revenue loss for the company. Because 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 are clear:

  • Higher sales conversion rates
  • Lower digital onboarding costs
  • Increased protection from fraudsters and spoofing attacks

Toward a more frictionless user experience

PXL Vision’s passive liveness solution is optimised for conversion, which means it causes significantly fewer dropouts than competitor solutions. 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 digital onboarding costs and improve its attack detection and access control with PXL Vision’s passive liveness method.

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