Did you know that liveness detection prevents spoofing?



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The facial recognition is the perfect biometric system for authentication in mobile devices and tablets.

It is a fast technology, very easy to use and more secure than traditional passwords.

The expansion of this type of authentication method has unleashed the creativity of cybercriminals.

One of the most common facial recognition scams is identity theft or identity theft. Specifically, spoofing is a type of presentation attack.

Hence, to the improvement of technology, resources must be added to prevent and avoid associated fraud.

Liveness detection significantly reduces spoofing attacks, among other presentation attacks.

What is it?

In the field of biometrics, liveness detection is the technology that detects if there is a person behind an operation that involves biometric recognition.

As its name suggests, it detects if there is life (and, therefore, if there is fraud or not), it checks that there is a human performing a specific action.

These mechanisms prevent attacks through “D or 3D masks, photos, videos, etc.

There are two types of liveness detection, depending on the method used:

  • The active liveness detection requires the end user to perform certain movements or gestures in front of the camera, from blinking to gesturing or making head movements.
  • For its part, the passive liveness detection detects if there is life, but without the collaboration of the user. From the capture of biometric data, it is able to analyze them and thus detect if there are signs of fraud or not.

Passive liveness detection vs. active liveness detection

Although until now it was ensured that the end user had to encounter some barrier during their verification to trust the technology (and, incidentally, scare off fraudulent users).

The truth is that more and more companies are moving away from the active liveness detection.

Not only because passive liveness detection solutions are more robust, but because user experience (usability) is increasingly prioritized.

Technically, passive liveness detection requires smaller implementations in terms of user interface than active liveness detection (usually requiring some kind of software modification) and detracts from usability.

One of the most important points is that passive methods are stronger against spoofing, since the fraudster does not know how the fraud is being detected. While the active methods have hints, which can guide you.

A passive solution with proven robustness is preferable to an active liveness detection solution. Both types accurately detect a variety of fakes, but only passive liveliness keeps the process fast and effortless. The fact that companies increasingly prioritize user experience as a way to attract and retain customers is driving the shift from the active solutions of yesterday to the modern passive liveness detection nowadays.

How does it work?

In the case of liveness detection from passive mechanisms, it works by analyzing image and video signals. Sometimes even with specific hardware, and even combining the use of multiple detectors based on machine learning and computer vision.

The end user only has to perform the “selfie” pose associated with facial recognition. From this position or pose, the system assesses the risk of impersonation. It is an automated process and integrated into facial recognition, therefore it is not perceptible by humans.

As for active mechanisms, they use techniques based on the challenge-response principle. They ask the user to take a certain action and evaluate the user’s response to make a decision.

The Alice Biometrics Passive Liveness Detection

At Alice we opted to fight identity theft with a robust identity verification solution based on passive techniques. Therefore, our liveness detection does not require any kind of collaboration from the user.

We don’t want to give cybercriminals clues about the methods we use to catch them. In fact, our solution is unnoticeable and scammers will not know that their actions are being evaluated.

We use vivacity detection as an additional security barrier to provide the strongest fraud detection in our digital onboarding and remote identity verification processes.

We are committed to the videoselfie in our biometric identification to offer our clients a solid analysis of images and videos, which detects identity theft attacks more easily.

This mechanism allows us to confirm that there is a living and legitimate person behind the registration process, who does not intend to carry out any irregular or illegal activity.

It helps us to eradicate identity theft fraud because the cybercriminal is not able to advance in the process. Therefore, we consider it our main weapon to face identity theft.

Advantages of our passive liveness detection

  • Integrates in a matter of minutes through the REST API
  • Compatible with web and mobile platforms
  • Flexible, it can be combined with other technologies
  • Analyze images and / or videos
  • Provide an assessment of the risk of spoofing based on the most advanced techniques

In addition, we focus on our clients optimizing their services to offer frictionless experiences (to reduce churn rates) and geared towards customer conversion.

If you want to reduce costs and improve the registration of your clients, you need our technology. Let’s talk ?

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