Liveness DetectionUsing Live Human Features for Enhanced Verification

The Top 10 Liveness Detection Benefits for Merchants & Consumers

Scammers can engage in a lot of creative practices to bypass your security.

For instance, using high-quality 3D-printed fingerprints or digital recreations of a person's face. These are called presentation attacks. And, they’re very real challenges that security experts have to overcome. Liveness detection is one way to combat these threats.

Also referred to as “anti-spoofing” or “liveness verification,” this technology helps you tell authentic biometric data from spoofing attempts. Today, let’s examine how this works, and what value it can offer you.

What is Liveness Detection?

Liveness Detection

[noun]/līv • nes • dē • tek • SHun/

Liveness detection is a pivotal security feature in different authentication processes, including biometric verification. Its primary function is to confirm that the individual being authenticated is physically present during the biometric data capture.

Liveness detection helps prevent biometric spoofing by verifying that biometric data — like a face, palm, iris, or fingerprint — actually originates from a real, live person rather than a phony duplicate.

The technology used to tend more toward rules-based approaches that were static and unchanging, which made them less effective against emerging threats like AI-generated deepfakes.

Newer, machine learning-based liveness detection systems, meanwhile, are trained on massive datasets, making them capable of detecting subtle anomalies from the outset. They’re also able to continuously improve over time. These two signature strengths make machine learning systems comparatively more effective than rules-based systems at combatting deepfakes and other advanced threats.

How Does Liveness Detection Work?

Liveness detection uses a variety of datapoints to determine whether the biometric information submitted by a user is genuine or not.

These systems rely on algorithmic analysis to match a provided sample with a pre-registered one. It can automatically adapt to alterations in an authorized user's face, such as the presence of glasses or facial hair. It can also improve over time by ingesting and incorporating new data.

The idea is to pair machine learning techniques with indicators like:

Types of Liveness Detection

Liveness detection can take one of two basic approaches.

There’s passive, which is the fastest and least intrusive approach. It operates in the background and does not require the user to take any additional actions to authenticate themselves. On the other end of the spectrum is active detection. This approach is the slowest, but also more secure.

Liveness Detection Workflow

Liveness Detection Benefits for Merchants

Now, here’s the million-dollar question: what are the practical benefits of adopting liveness detection? Here are just a few of the ways you stand to benefit:

Balancing Security With User Experience

Deciding whether passive liveness detection is sufficient for your needs, or whether an active approach will be the better choice, will depend on your risk tolerance level and desire for a frictionless checkout experience.

If you are willing to accommodate some risk in exchange for a smooth user experience, passive liveness detection is probably good enough. Because these systems work in the background to verify users, your customers won’t even know it’s there.

An active system would work better for you if you have a low tolerance for risk. For instance, maybe you’re experiencing a high baseline level of fraud and don’t have the appetite for more. Or, perhaps you’re selling high-value products or services, and can’t afford to lose even a single sale to fraud.

Either way, make sure to closely monitor checkout conversion and cart abandonment rates. Active liveness detection may tend to result in lower conversion rates and higher cart abandonment rates. This is normal, since an active approach inherently introduces more friction. Your best bet is to conduct routine A/B testing to see which approach yields the best tradeoff in terms of conversion and fraud rates.

Integration With Existing Systems

Implementing liveness detection shouldn’t require a complete overhaul of your site. Most leading providers will offer plugins and extensions for popular eCommerce platforms like Shopify, Square Online, Squarespace, WooCommerce, Wix, BigCommerce, Ecwid, and Magento.

Implementation Readiness Checklist:

• Current fraud rate documented
• IT team availability confirmed
• Customer service briefed
• Legal/compliance review complete

Ready-made integrations allow you to quickly install, configure, and deploy a liveness detection system, often in just several few clicks. This means you can add a powerful layer of security to your account creation or checkout process with minimal effort or technical knowledge.

For more customized needs, providers offer robust APIs (Application Programming Interfaces) and SDKs (Software Development Kits) that let you seamlessly integrate liveness detection into your existing systems. This gives you full control over where, when, and how identity verification checks occur.

As for implementation timelines, you can expect to be up and running within hours or days. Costs can vary depending on the provider, your transaction volume, and the level of protection you want; anywhere from $20 per month for starter plans to more than $10,000 per month for enterprise-level solutions.

Some liveness detection services also offer pay-per-use fee structures. Here, costs can range from $1 per 1,000 liveness checks to upwards of $10 or $15 per 1,000 tests.

Liveness Detection Isn’t a Singular Solution

Liveness detection enables better security and a more user-friendly experience. But, the technology is not foolproof. Attackers have sometimes used realistic masks, photographs, and even deepfake videos to bypass security measures.

This is no trivial weakness, especially in light of the fact that deepfake attacks have exploded in prevalence. In fact, data from Signicat shows that deepfake attacks now account for 1 in 15 or 6.5% of all fraud attempts; an increase of 2,137% over the past three years. Liveness detection can help… but it’s not a panacea.

When considering your biometric authentication strategy, of which liveness detection is just one component, it is crucial to be aware of these potential limitations. A balanced view of the strengths and weaknesses, aligned with your specific needs and risks, is essential for developing an effective and user-friendly authentication system.