The idea of liveness relative to biometrics dates back to the early 2000s. Prior to AI-driven liveness detection, companies relied on humans to perform liveness checks. For example, during remote onboarding, customers would be required to show their photo ID on a video call to validate the physical presence of the person in the ID. Of course, this process was slow, costly, and error prone.

The technology behind liveness detection is based on the recognition of physiological information as a sign of life. Historically, liveness algorithms have been trained to identify head movements, dilation of a subject’s pupils, changes in expression, and other physical responses.

The first generation of facial liveness technology is referred to as “active.” Active liveness detection relies on the user’s movements in response to challenges such as nodding, blinking, smiling, or correctly positioning one’s face in a frame. While the technology can be effective at detecting a spoof, it introduces friction into a verification process that was largely desirable for its ability to remove friction and is less secure as fraudsters have learned how to fool these systems. The pursuit of an easier solution, facilitated by increased access to training data for better machine learning, led to a new generation of “passive” liveness detection.

Passive liveness is fundamentally different from active in that it requires no action by the user. As such, active liveness is impractical for use cases with frequent login. The friction also has a negative impact on new customer acquisition, with companies reporting abandonment rates as high as 50% when using active liveness. 

However, there are additional differences to be aware of when comparing the two approaches.

A variety of techniques are used to perform a passive liveness check, ranging from analyzing a selfie image to capturing a video, to flashing lights on the subject. These passive liveness approaches have different impacts on the user experience and processing as outlined in the following table.

How can facial liveness be determined based on a single image? It starts with the ability to use the same selfie image that is used by the biometric and facial recognition system. As such, no special hardware or additional software is needed for image collection. If the quality of the selfie is good enough for facial recognition, it can be used for the liveness check.

The other enabling factor is use of Deep Neural Networks that process the selfie image to detect artifacts that help distinguish between a photo of a live person and a presentation attack. ID R&D has built a unique DNN-based approach to liveness detection that underpins the single image technique.

The process takes less than one second and looks like this:

A presentation attack involves “presentation” of non-live facial imagery to the camera during biometric capture, instead of a live selfie. An injection attack is conducted by “injecting” digital data downstream from the camera by way of hardware and/or software hacks. A digital representation of a live capture can spoof liveness detection if it does not know the imagery is not from the camera.