Passive vs. Active Liveness Detection in Facial Recognition

25 April 2025

With deepfakes and spoofing attacks on the rise, liveness detection - also known as liveliness detection - is now one of the most important things businesses consider when considering a facial recognition system. 
But not all liveness checks are created equal. The debate between passive and active liveness detection is more important than ever - especially as fraud tactics evolve.
Let’s unpack what liveness detection really is, how it works, and why passive approaches are fast becoming the industry standard for both security and user experience.

What is Liveness Detection?

Liveness detection is a technique used to determine whether a biometric sample - usually a face - is being presented by a real, live human at the time of capture. Its purpose is to prevent fraudsters from using photos, videos, masks, or synthetic content to impersonate someone.
In simple terms, it’s how face recognition systems make sure they’re seeing a real person, not just a convincing fake.

Why Liveness Detection Matters

Biometric authentication works by matching a user’s unique facial features to a previously stored template. But what if someone presents a printed image, or worse, a deepfake video?
That’s where liveness detection comes in. Without it, biometric systems are vulnerable to presentation and injection attacks. And with generative AI making deepfakes easier and more realistic, robust liveness detection is no longer optional.

Active vs. Passive Liveness Detection

There are two main types of liveness detection used today: active and passive.
Active liveness detection requires the user to perform a specific action - like blinking, turning their head, or smiling. These checks are intended to confirm that the person is real and not a static image or basic video.
While active liveness is widely used, it’s increasingly being outsmarted by deepfake technology, which can simulate facial movements. It also interrupts the user experience, making the process slower and more cumbersome.
Passive liveness detection happens invisibly in the background. The system captures a user’s face as usual, and then runs analysis in real time to determine if the face is genuine. It uses indicators like natural light reflection on skin, depth mapping, skin texture, and subtle micro-expressions.
Because it doesn’t require the user to do anything extra, passive liveness is smoother for the end user. And because it relies on subtle, hard-to-replicate signals, it’s far more resilient to deepfakes.
It also offers the ideal balance between security and usability. It removes friction from the user journey while dramatically improving fraud resistance.
Unlike active liveness, passive systems don’t rely on observable behavior that can be faked. Instead, they look at biometric signals that deepfakes struggle to reproduce - such as how light bounces off real skin, or the 3D structure of the face.
It’s faster too. Keyless’ passive liveness engine performs checks in under 300 milliseconds - five times faster than many competitors - making it nearly invisible to the user.

Fighting Deepfakes with Passive Liveness

Deepfakes are no longer a niche threat. They’re a mainstream attack vector - and they’re only getting more realistic. Face recognition systems need to evolve accordingly.
Passive liveness detection is the most effective and user-friendly way to ensure that the face being scanned is live and real. Combined with behavioral analysis and device-level intelligence, it can provide strong defense against even the most advanced threats.
Active methods are increasingly outdated. Deepfakes can simulate eye blinks and head movements with ease. But replicating the nuances of a real human face in real time - like skin texture, depth, and light behavior - is still extremely difficult. That’s where passive liveness shines.

Keyless’ Three Lines of Defense Against Deepfakes

At Keyless, we’ve developed a multi-layered approach to fight the growing threat of AI-driven fraud. Our liveness detection is just one part of a comprehensive security stack designed to stop both presentation and injection attacks.
  1. First, we use genuine identity assurance, linking a user’s face to a specific device. This means even a perfect deepfake can’t authenticate unless it’s presented from the original trusted device.
  2. Second, we deploy certified passive liveness detection to spot fake faces, masks, photos, or deepfake content shown to the camera.
  3. Finally, we’ve launched our newest line of defense: injection attack detection. This capability uses machine learning to detect tampered device behavior and analyze biometric inconsistencies. It’s built to stop deepfakes that are injected directly into a device’s video feed, bypassing the camera altogether.
Together, these three layers ensure that users are not only present—but also real, trustworthy, and in control of their device.

Certified and Compliant

Keyless’ face recognition and liveness detection systems are certified to the highest standards in the industry:
  • ISO/IEC 30107-3 compliance for presentation attack detection.
  • FIDO Biometric Certification for anti-spoofing and privacy compliance.
  • NIST FRVT 1:1 verification, confirming extremely low error rates.
These certifications are essential for serving high-risk industries such as banking, fintech, crypto, and government.

Ready for What’s Next

As generative AI advances, biometric systems need to stay a step ahead. That’s why we continue to invest in AI-resistant technologies that can adapt to new attack patterns in real time.
Our latest release - injection attack detection -  addresses one of the fastest-growing threats in the fraud space. And it does so without compromising user privacy or performance.
Liveness detection is no longer just a checkbox feature - it’s the frontline defense for any face recognition system. And in that fight, passive liveness detection is leading the way.
It’s faster, stronger, and harder to fool. It works silently in the background to protect users without slowing them down. And when combined with device-level security and injection attack detection, it forms a powerful shield against modern fraud.
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