Facial liveness detection: how it works & why it matters

Biometric systems, including facial recognition, are popular layered defenses against fraud and impersonation attempts. But as these fraud attempts, like deepfakes, become more sophisticated, simply recognizing a face is no longer sufficient. This is where facial liveness detection comes in: the process of verifying both that a face matches a biometric template and that the live person to whom that face belongs is present in real-time and is the one interacting with the device or system. 

For organizations that rely on digital onboarding processes, remote access, and similar scenarios, liveness detection provides an additional safeguard to identify verification. Within Mitek’s solutions, this is a strategic layer of identity and fraud prevention. 

To understand the true value of liveness detection, we’ll walk through what it means, why it matters, the attacks it helps prevent, the scenarios where it is (and isn’t) necessary, the considerations that influence solution selection, and the technology trends driving its evolution. 

What is facial liveness detection? 

Facial liveness detection is also referred to as face-liveness detection, face presentation attack detection, or sometimes simply “liveness detection” due to it being the most common type of biometric liveness detection. Facial liveness detection is a process that allows a biometric facial recognition system to determine whether the face being presented is the face of a live human who is physically present in front of the sensor, or whether what is being presented is a spoofed face (like a photo, screen, video, mask, or deepfake).  

While there are other forms of liveness detection, including document liveness detection to determine if an identity document is truly physically present, and other types of biometric liveness detection like voice liveness, facial liveness detection is especially critical due to the prevalence of cameras on mobile devices and laptops that’s resulted in the use of facial biometrics in so many day-to-day authentication workflows. 

A facial biometric authentication workflow typically will capture the face, extract biometric features, match those features to the template, and grant or deny access accordingly.  Facial liveness detection is inserted into this workflow to introduce a new checkpoint: before or during the matching step, the system will run additional checks to determine if the biometric sample is live, and that the person is there and interacting, rather than the sample being from a replay, another person with masks or prosthetics, or a deepfake.  

In short, facial liveness detection is the combination of facial recognition with anti-spoofing (presentation attack) detection and live-presence verification. 

Why facial liveness detection is important 

If a liveness check is not performed, facial recognition systems can be vulnerable to several types of spoofing attacks. The addition of facial liveness detection provides several benefits: 

Strengthening biometric authentication: Biometric authentication systems are considered stronger than passwords or tokens because they’re dependent on “who you are”, versus “what you have” or “what you know”. It’s far easier for a fraudster to steal a password than a face. But “who you are” can be faked with varying levels of sophistication through the use of photos, videos, masks, or prosthetics. Liveness detection layers, “Are you really present now?” atop the “Who you are” element, making biometric authentication more trustworthy. 

Fraud and identity theft prevention: Remote account onboarding, mobile check-in, and other digital transactions are all vulnerable to fraud via the use of stolen identity documents. Facial liveness detection can thwart presentation attacks and ensure that the face presented is live and currently interacting with the camera. 

Meeting KYC and AML compliance requirements: Incorporating facial liveness detection ensures robust anti-fraud measures and helps institutions like banks and FinTechs satisfy Know Your Customer (KYC) and anti-money laundering (AML) requirements, while boosting trust and reducing overall risk. 

Staying ahead of deepfakes and synthetic media: Synthetic (AI-generated) media continues to improve in quality, and technology like 3D printing is also readily available and much lower cost than it was when it was an emergent technology. Systems that simply match faces will, eventually, fail when confronted with high-quality synthetic media or prosthetics. Facial liveness detection capable of detecting anomalies that a face match or even a human observer might miss is a key tool that allows institutions to stay ahead.  

These benefits make it clear that facial liveness detection is, increasingly, a foundational capability for building a secure identity verification workflow. 

What facial liveness detection can protect against 

Facial liveness detection can mitigate specific threats with presentation attack detection and broader spoof detection. We mentioned many of these broadly earlier in this blog, and will describe them in depth here: 

Presentation attacks: These occur when someone presents an artifact to the camera or sensor that impersonates a legitimate user. It might be a printed photo, a video or static image replay from another device, or a 3D-printed mask or prosthetic. With a standard face recognition engine, this attack might succeed at matching the user's face. Facial liveness detection is designed to detect the lack of motion and depth, texture anomalies, and other defects that flag the attempt as non-live. 

Deepfakes and synthetic faces: Powerful AI video tools are now available to the general populace at low or even no cost, making it much easier for attackers to generate high-quality synthetic faces and deepfakes. Facial liveness detection systems have evolved to meet this challenge by detecting not just static spoofing but synthetic or manipulated video content, successfully distinguishing genuine live content from this manipulated media. 

Replay and injection attacks: Attackers with more technical acumen may replay previously captured video of the legitimate user, or inject a feed directly into the system. The use of facial liveness detection guards against this by detecting anomalies in the injected content to distinguish replays from live video. 

The ultimate goal of all of these types of attacks is to perform identity theft or impersonation, so that users can commit account takeovers or fraudulent account onboarding. Without liveness detection, these techniques can be used to submit stolen identity photos and pass face matching to create new accounts or access existing ones.  

When to use it and when not to use it  

Use cases for facial liveness detection 

Facial liveness detection is highly valuable in scenarios like: 

• Remote customer onboarding, especially in banking, fintech, insurance and telecom industries 
• High-risk access control, for financial portals, administrative dashboards, and sensitive data or systems in any type of organization 
• Device and app logins, where facial recognition is commonly offered as an option and a liveness check can ensure that the account owner is present and actively participating in the login process 
• Border control and travel identity, especially at automated border kiosks or within mobile travel apps 
• Passwordless authentication in general, as organizations move away from the use of passwords and seek a strong security vector 

In all of these instances, facial liveness detection provides heightened security for authentication while seamlessly integrating into the authentication process. 

When not to use it (or use with caution) 

Despite its usefulness, facial liveness detection may not always be appropriate or practical. In low-risk workflows that don’t involve access to sensitive data or to financial accounts, the added overhead may not deliver significant ROI or be needed to ensure user peace of mind.  

User experience-constrained environments can result in poor performance that requires fallback workflows, and offline or no-camera environments (e.g., secure government areas where connectivity and cameras are banned, or very remote/isolated locations without connectivity) will make liveness detection infeasible even when additional security is desirable. And legacy systems with poor biometric support may experience integration friction.  

Decision factors 

When deciding whether to include facial liveness detection in a specific authentication workflow, be sure to consider the risk level of the transaction/access point, your organization’s general level of fraud exposure, device and environmental constraints that might limit its efficacy, and any regulatory obligations you might have. Many organizations find that any remote face-recognition workflow that has moderate to high fraud risk and doesn’t involve offline/cameraless environments is an ideal candidate for facial liveness detection.  

How facial liveness detection works 

Facial liveness detection uses computer vision, machine learning, challenge-response flows, sensor cues and UX design to deliver a decision in real-time. Here’s how the process works: 

1. Capture and pre-processing: In this step, the user presents their face via camera, whether it’s on a mobile device, tablet, or desktop or laptop. The system first detects the face, then identifies landmark features and bounding boxes. It will pre-process the image for alignment, normalization, and cropping, and perform quality checks for things like lighting, image resolution, and whether the person may have moved and created image blur. Captures can be performed either passively or actively. Passive liveness detection is considered the superior approach because it runs invisibly in the background, requiring no explicit actions from the user making it seamless for legitimate users and far harder for fraudsters to anticipate or exploit. Active liveness detection, by contrast, is higher friction and asks users to perform specific actions, such as blinking, smiling, or moving their head in certain directions.

2. Liveness detection and presentation attack detection (PAD): The core liveness check uses multiple techniques to assess the image. Texture and frequency analysis look for micro-texture difference between real faces and artifacts, including skin micro-structure, blur, or color banding. Motion and optical flow analysis may look for signs of the micro-movement and micro-expressions seen in real faces. Depth and 3D structure assessment infers depth from mobile cameras or uses depth sensors, to ensure the face has a genuine 3D structure and is not a 2D printed image. Sensor-based cues leverage advanced imaging algorithms to detect masks or prosthetics that might pass a 3D test, but aren’t real skin. Passive liveness systems use AI-powered algorithms in the background to perform these checks without explicit prompts, and use machine learning to detect anomalies. 

3. Face recognition and matching: Once the liveness check is passed (or in parallel), the system extracts biometric features and performs face matching by comparing the face to a previously enrolled template or checking against a database. Once face matching and the liveness check are both satisfied, the system will grant access or proceed with account onboarding. 

4. Decision and workflow integration: The outcome of the liveness check and face matching can trigger different flows depending on risk levels. While a pass will allow access, a failed check can deny access, flag the anomaly, and escalate the activity for manual review, or fall back to another verification method. Low-confidence matches also might trigger other workflows, like a second check or an alternate method.  

5. Analytics and monitoring: For compliance as well as for process optimization, modern systems keep logs of every interaction that track things like spoof detection rates, false positives or false negatives, and fraud event correlation. This data can be used over time to refine machine learning models, identify areas for improvement or potential areas of bias, document KYC/AML compliance, and improve the user experience. 

Facial liveness detection considerations 

Deploying facial liveness detection requires weighing multiple considerations, ranging from security effectiveness to practical implementation. 

Accuracy and third-party testing 

Accuracy is key – a system that fails to reliably distinguish attacks will enable fraud, while one with too many false positives has a negative impact on the user experience and on internal workflows. In a study by Mitek, AI correctly identified biometric spoofs in 96% of cases – which far exceeded the human rate of 61%. However, the research notes that performance on controlled datasets may not always generalize well to unknown attacks in the wild. While evaluating solutions, prioritize those that have undergone third-party testing, are robust across multiple types of devices as well as lighting conditions, document their false positive and false negative rates, and have the ability to leverage modern capabilities like AI and machine learning to keep models updated as new fraud techniques emerge. 

User experience and performance 

To ensure a positive user experience, seamless security checks — working frictionlessly and in real-time - are also important, especially in customer-facing environments. The same goes for performance in real-world conditions that include inconsistent lighting, older cameras, and devices held at varying angles. Software should also perform well on diverse user populations. Additionally, clear workflows with fallback options for failed liveness checks help maintain the user experience without compromising security and reduce the inconvenience for users who generate false positives. 

Cost and implementation 

Cost is another consideration, and extends beyond licensing fees. Organizations must understand their per-transaction costs (for example, for API calls), implementation and integration expenses, performance requirements that might include upgraded camera hardware or additional compute, and other operational overhead – like false rejects that create manual review costs. A cost/benefit analysis would include the incremental cost of liveness detection against the expected value gained by reducing fraud, avoiding regulatory fines, and saving reputational value. 

Accessibility and device compatibility 

Because facial liveness detection depends on camera input, organizations where the user environment is uncontrolled (“bring your own device” workplaces, or B2C companies whose customers are using their own devices) need to understand the percentage of their userbase that might utilize older, suboptimal devices like older mobile phones and select solutions with this in mind, including building in adaptive flows for these users. In building accessibility workflows, it’s important to enable facial capture in both landscape and portrait modes in the case that a device is mounted an unable to rotate.

Privacy, security, and compliance 

To maintain privacy and data protection, organizations must also understand what data is captured, stored, and/or transmitted during the facial liveness detection check, whether that data is processed locally on-device or in the cloud, how long images are retained, and what user consent mechanisms are in place or must be put in place. Legal compliance with regulations like GDPR and CCPA requires a close look at how vendors approach template protection, data encryption, and overall security. 

Future-proof facial liveness detection solutions and easy integration 

Solutions should be able to adapt as quickly as fraudsters do. Effective solutions will include regular updates to their detection model, including the use of AI and machine learning to detect and stop emerging spoofing techniques, especially as deepfake attacks become more convincing every day. They should be capable of tracking attack patterns and heuristically detecting previously unknown attack methods that were not explicitly seen during their training.  

Finally, integration capabilities are needed. Institutions should consider where the liveness check occurs in their workflow – is it pre-match, post-match, or in parallel? The system should also seamlessly connect to defined paths when liveness detection fails, whether that’s a fallback verification method or manual review. The system should also hook into comprehensive dashboards that include conversion rates, fraud rates, user drop-off, and any other indicators of friction, alongside your broader goals for identity verification and KYC and AML compliance. With integration that feels native to your workflow, adoption will be far more effective. 

Future trends in facial liveness detection 

More sophisticated technologies that are capable of producing quality deepfakes and other media capable of biometric spoofing are available every day, and facial liveness detection has adopted AI- and machine learning-powered algorithms to keep up with the quantity and quality of attacks. These AI-powered systems can outperform humans in detecting the kind of artifacts present in spoof attempts.  

AI-driven spoof detection 

For the next generation of AI-driven liveness detection, systems will train on large-scale and diverse spoof datasets that encompass the usual photos, videos, and masks, plus wide ranges of deepfakes created by widely available generative engines like Sora 2 and Nano Banana, as well as lesser-known tools. The systems will also be built with the capability to learn and adapt in real-time, as the speed of technological development ensures a steady stream of zero-day attack vectors that won’t have been seen in training. 

On-device and hybrid processing 

We’ll also see more lightweight models, including those that can run on-device and reduce latency while mitigating privacy concerns related to data storage and data in transit. These may include hybrid architectures that combine edge processing and cloud-based intelligence, as these capabilities mature. This can also expand the use of facial liveness detection into situations like the ones we previously discussed, where connectivity is limited or unavailable. 

Expanding industry applications 

The range of applications for facial liveness detection is also likely to widen. The use of digital identity has spread into healthcare, automotive, IoT, and other emerging markets.  

Regulatory standardization and transparency  

Regulatory bodies may also create standards that result in higher standardization and quality, irrespective of industry. And for the customer or client, user experience innovation will make facial liveness detection increasingly seamless. The ideal future system includes a real-time, passive liveness check that adapts to older devices, varying lighting conditions, and any changes in the user’s appearance, with fallback flows rarely used. 

All of these trends point toward a future where facial liveness detection is more accurate, more widely used, and more transparent. For vendors and those who implement liveness detection, the result should be higher accuracy and seamless real-time execution of the process of verifying that the person on the other side of the camera is live, present, and who they say they are. 

Building trust through facial liveness detection 

Verifying that a face is live and present, rather than spoofed via photo, video, mask, or deepfake, enables organizations to dramatically reduce fraud in a way that maintains a seamless user experience. Fraudsters have a wide variety of methods from which they can choose to pass a facial biometric check, ranging from simple presentation attacks with printed photos to the use of elaborate 3D prosthetics or sophisticated AI-generated deepfakes. Adding a liveness check to biometric face recognition elevates accuracy and trustworthiness. 

For this reason, organizations must deploy a layered approach that protects against the full spectrum of attack vectors. Anything less leaves the door open as fraudsters actively look for gaps and exploit the methods most likely to succeed long before an attack is detected. Incorporating a liveness check into biometric face recognition is a critical part of a larger defense strategy, significantly enhancing both accuracy and trustworthiness. 

Protecting What’s Real 

In an era where trust is a business imperative, Mitek brings together the critical capabilities organizations need to create a seamless, secure experience end-to-end. Whether it’s swift digital onboarding, instant identity verification, or safeguarding high-risk transactions, our technology welcomes customers with confidence and protects them at every step of their journey. 

As threats evolve from deepfakes to emerging AI-driven manipulation Mitek provides enterprise-grade defenses that keep pace. Our leadership in passive facial liveness detection, spoof detection, deepfake prevention, and presentation attack detection is backed by years of deployment in highly regulated environments, where accuracy isn’t optional and trust must be earned every day. It’s why some of the world’s largest and most respected enterprises rely on Mitek, and why millions of consumers experience the peace of mind our solutions are designed to deliver. 

At Mitek, the mission is simple but essential, to protect what’s real.