Liveness detection is a technology widely used in modern identity verification processes, especially now that emerging threats like deepfakes are more complex and realistic. Thanks to AI and its wide, mainstream level of accessibility, generating funny pictures or using deepfakes purely for entertainment purposes has gone far beyond its original scope and is now widely used by fraudsters online to forge IDs and create realistic deepfakes for bypassing Know Your Customer (KYC) systems.
That’s why liveness detection is the go-to technology in facial biometrics, helping companies like banks and other regulated industries to onboard customers and check if the captured selfie is real and belongs to a genuine customer, not a spoof or a fraudster wearing a mask. Without this technology, identity verification workflows would be ineffective or at least bypassable at some point, especially when only a single security layer, such as a document check, is used. In high-risk industries like crypto, where fraud is an issue and large transactions are common, liveness detection in identity verification is essential to stop bad actors.
But how does it really work, and what does liveness detection mean for online payments or everyday risk management, such as account takeover prevention? We discuss all the main things linked to this technology in a simple and brief way below.
What is Liveness Detection?
Liveness detection is a technology designed to assess whether a user’s face and facial biometrics captured during the selfie identity verification process are real. The user is assessed by the system using liveness detection automatically, determining whether they are real and actually completing (physically present) the verification process in real-time.
Generally, liveness detection verifies whether the person isn’t a spoofed or a fake, synthetic source, such as:
- A wax/silicone doll
- A printed paper mask
- A partial head artifact, such as a wax head
- Unnatural shadow (common for deepfakes)
- Other traces, such as light distortion or excessive glare
This technology has been the number one weapon against deepfakes. That’s why proper liveness detection systems protect businesses against fraudsters who use lookalikes, altered or stolen digital images and videos, as well as other forms of fraud, such as AI-generated avatars, video injections, or high-resolution video replays.
What are the Types of Liveness Detection?
There are different types of liveness detection that differ based on the end-user experience and the overall working mechanism of the technology.
These include:
Active Liveness
Active liveness detection is based on prompts, meaning the user is required to interact and do some sort of movement, for example, blink, hold a number of fingers, smile, or move the head in a certain direction. As a result, active liveness checks can be less user-friendly, but they are considered to be highly effective against fraud and deepfakes due to their dynamic nature. Facial movements can’t be known prior, which makes active liveness checks almost impossible to bypass.
Passive Liveness
Passive liveness is based on asking the user to capture and submit a selfie. Its main distinction is that it runs in the background and doesn’t affect the user — that’s why it’s called “passive” — as no additional input is required. Compared to active liveness, this is a more low-key and seamless process. Passive liveness checks evaluate selfie texture, depth, and overall quality. However, users with lower-end smartphones or cameras can struggle to meet quality standards, which can also affect KYC conversions.
Hybrid Liveness
Hybrid liveness is an extra category that blends both liveness detection types, using hybrid/semi-passive checks. For example, the user is asked to complete a single step, such as smiling at a certain time while capturing their selfie. The main goal here is to strengthen security while still keeping the ID verification experience frictionless, much like passive liveness checks that minimize the hassle for the user.
How Does Liveness Detection Work in Biometric Verification?
Liveness detection works by combining advanced biometric algorithms and automated systems that can examine the image data (facial proportions, skin texture, or glare) with other factors, such as reflexive signals (breathing and blinking), and sometimes, metadata (the device information or geolocation), depending on the particular solution. In biometric verification, liveness checks can scan for spoof artifacts, helping detect non-human attributes.
Liveness detection verifies that the user’s:
- Face isn’t a mask, a printout, or a deepfake.
- Facial biometrics captured by the camera belong to a live person completing the ID verification.
- Expressions are genuine, for example, binks, head movements, as well as light reflections.
This is possible because neural networks scan the user’s face during a liveness check and create a 2D or 3D facial map unique to that particular face, assessing the facial features. For example, passive liveness checks often rely on 2D facial maps collected from a single selfie, whereas 3D liveness is based on a more complex workflow, prompting the user to move or rotate their head (which is why the system needs to capture more data).
Ultimately, 3D liveness is good for transaction approvals, while 2D checks are appropriate for simpler tasks like unlocking an app. By combining these elements, liveness detection systems can distinguish real users from synthetic content, including AI-generated videos that mimic human behavior with realistic details, which are now easily generated using widely available apps and free AI tools.
Related: Top 5 Use Cases of Biometrics in Banking
Why is Liveness Detection Important?
Liveness detection is important for companies that are required to secure their transactions and improve the account opening process with identity verification. Part of that is effective fraud prevention and KYC solutions, which include verifying user identities through ID document checks and biometric checks, powered by active/passive liveness technology. Otherwise, without proper verification, businesses risk accepting fake or stolen identities, a common tactic among scammers, especially since most financial platforms, such as crypto or trading apps, now require KYC checks.
It’s important to use a combined approach to KYC and use biometric verification with liveness detection due to security challenges like:
- Generative AI threats, such as synthetic identity fraud and document forgery.
- Technology limitations, for example, when only one layer of verification is used or 2D liveness detection instead of 3D checks is used for high-risk cases.
Effective identity verification requires a comprehensive system that addresses all potential risks. So, integrating liveness detection into identity verification and user onboarding allows businesses to identify and block even the most sophisticated bypassing attempts when fraudsters use more complex techniques that are not easily detected by the human eye.
Popular Use Case Examples for Liveness Detection
There are multiple scenarios where businesses use identity verification systems, including biometric verification with liveness checks, to onboard and verify user identities faster while rejecting fraudsters and attempts to access services like banking apps fraudulently. However, this isn’t the only use case.
Other examples of liveness detection and its uses include:
1. User Onboarding on Crypto Platforms
Crypto exchanges and all Virtual Asset Service Providers (VASPs) are required to conduct proper KYC checks before allowing their users to register and open a wallet or start engaging with their services due to the elevated risks associated with the crypto sector. Prior to having these rules, crypto was known to have a higher fraud rate due to the fact that it offered a higher anonymity level than traditional financial services.
Now, users need to provide their full personal details, such as their name, age and address, as well as an ID document, utility bill (for Proof of Address) and complete the full workflow with biometric verification and liveness detection, often also paired with database cross-checking and AML screening (for example, against PEPs and sanctions lists) to see if the user is in line with the platform’s risk level and isn’t using forged IDs or deepfake technology to bypass security checks.
2. Fraud Prevention for Financial Services
In strictly regulated sectors, like banking or fintech, identity verification with liveness checks is required at the account creation stage and later on throughout the whole business relationship, as part of the risk-based approach to AML compliance and the need to continuously monitor changes in client risk profiles. This is vital, as even the “best” customers with a clean history can develop bad tendencies and become high-risk customers, who require extra scrutiny and Enhanced Due Diligence (EDD) measures. Examples where these checks apply include Politically Exposed Persons (PEPs), customers from high-risk countries, cash-intensive businesses, or those with complex ownership structures.
However, there are more standard cases where reverification is required, and a good approach for that is through a quick biometric check with liveness detection, as the user doesn’t need to enter anything manually and can confirm their identity again before high-risk actions like a large transaction or when they signal some sort of risk, like rapid shifts in account activity or a suspicious sign in from a suspicious foreign location, and need to go through a stricter check to confirm it’s really them before moving forward. This helps prevent fraud and money laundering, as well as proper risk management and ongoing compliance practices.
Related: What is Reverification? Explanation Guide
3. Account Takeover Prevention in E-Commerce
Since it’s hard to bypass KYC and other security measures, many bad actors target already existing user accounts in less-protected industries that still have monetization and online transactions. In this sort of bubble, e-commerce platforms become prime targets. Fraudsters use phishing and impersonation and other techniques to breach or steal data and take over accounts, then change IBANs to forward all of the funds into their own bank accounts or use linked payment cards to make purchases directly on the platform.
Without verification or any sort of prevention and blocking of suspicious accounts, victims then open disputes, and for the e-commerce business, this means additional costs and reputational harm that they need to deal with. A quick biometric verification step with liveness detection can prevent account hijacking, especially when enabled before high-risk scenarios, like updating sensitive information on the account (for example, an address or IBAN), logging in from a suspicious, new device, or attempting to proceed with a high-risk transaction.
Extra RegTech solutions, along with liveness checks, for example, Bank Verification with open banking technology that links the user directly with the bank and shares information, including IBANs, to cross-check and detect red flags, help reduce friction and make the verification process seamless, even in more complex and high-risk scenarios.
4. Remote Employee and Contractor Verification
Freelancers, contractors, sole proprietors, and other businesses that need to verify their employees remotely can do this easily through ID document and biometric checks with liveness detection. This helps save time on manual processes like document collection or signing, and can be combined with extra tools like iDenfy’s Criminal Background Check, a feature that connects to official criminal databases, running quick background checks to see if the individual doesn’t have any links to crime and is suitable to join the company.
This is vital in various industries, such as healthcare (especially since it handles patient data, which is prone to breaches and medical ID theft) or education, where some students alter and forge their documents due to visa issues or as a way to access financial benefits. In this sense, ID verification and liveness detection ensure that remote onboarding is secure, preventing impostors from using fake identities to gain access to internal systems or company data.
5. Multi-Factor Authentication (MFA) Enhancement
Relying only on multi-factor authentication (MFA) through one-time passwords (OTPs) as a single authentication layer is not sufficient in some cases, as bad actors use phishing, SIM swaps and other social-engineering techniques to move a victim’s phone number to their SIM to receive the generated SMS verification code.
However, this authentication measure, including alternatives like email verification through email OTPs, is a good option as a first step in a fuller identity verification process. For example, requiring a quick MFA and a liveness check before approving a payment or accessing sensitive data on any online platform ensures that only real users, and not bots or fraudsters attempting to bypass the security checks, can complete the process.
How Do I Choose the Best Liveness Detection Integration?
The most important and the main one is to choose a biometric verification solution that offers proper liveness detection technology, which is why you need to test out several KYC solutions and providers based on factors like a) accuracy rate; b) ability to onboard the user quickly; c) fraud detection capabilities, especially deepfakes; d) costs and flexibility in terms of your current workflow.
Other nuances that you might want to consider and tick as a box for a good liveness detection option are:
➡️ False Positives
Systems can sometimes flag legitimate users as fraudulent, which can negatively impact the user experience. Solutions like iDenfy’s ID verification service provide the highest accuracy rates with the ability to add manual review, which means that KYC specialists adjust such results and fix any software mistakes in real-time so that all legitimate users are onboarded without affecting their journey.
➡️ Conversions
Even active liveness checks should not damage the user experience entirely. The solution should be integrated with your current system easily, guiding the user throughout all of the steps so they know their progress and how to complete the verification correctly. For example, explanations that the user should avoid poor lighting to capture poor-quality selfies during the check are vital to keep a good UX.
The decision also depends on other classics like your operating industry, risk appetite, and particular scenario. For example, a popular example where liveness detection is beneficial is age verification in age-restricted industries, such as adult-only content apps or online shops selling items like vapes or alcohol. Liveness detection in biometric verification helps assess the person’s face, and combined with an ID document check on the side, can help quickly determine if the person is of age and can access the service or purchase the age-restricted item legally online. For example, iDenfy has a no-code age verification app with built-in liveness detection available for all Shopify merchants.
For other options, like face authentication with passive/active liveness or no-code magic link ID verification and other API and Mobile SDK integrations for KYC compliance, book a free demo.
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