What is liveness detection?
Liveness detection is a biometric security technology that verifies whether a person is physically present and alive. It is typically used as part of a customer onboarding flow to ensure that a prospective customer is who they say they are and to verify that their identity verification method is legitimate. Liveness detection is generally used in industries where digital security is paramount, such as in the finance or healthcare.
"Liveness detection ensures the face in front of the camera is from a real, live person — not a photo, video, deepfake, or mask," explains Yessica Correa, Daily Operations Analyst at Fourthline.
This technology has become an essential part of digital identity verification, as fraudsters develop increasingly sophisticated methods to bypass traditional authentication systems. By confirming that a real person is present during verification, liveness detection prevents various types of presentation attacks whilst enabling secure remote KYC onboarding for legitimate customers. Read on to learn how it works.
How liveness detection works
Liveness detection analyses biological signs of life. The process uses various methods, from detecting subtle facial movements to requiring users to perform specific actions that demonstrate real-time presence and responsiveness. These movements and actions are primarily analysed by AI systems, supplemented with human oversight when necessary to create multiple layers of protection against spoofing attempts.
"The first step of detection is automatic,” explains Correa. “The AI will make sure there is some sort of movement and liveness. It may check whether the client is moving their eyes, blinking, or breathing.” The system continuously analyses video input for signs of natural human behaviour that can't be easily replicated through static images or simple recordings.
When automated systems detect potential issues, human analysts like Correa step in to provide additional verification. "When the system can't see any movement for some reason — let's say the quality of the phone is bad — then it comes to the human reviewer,” she explains. “After that, we can examine the details of the movements to confirm that everything looks okay.”
This hybrid approach has a twofold benefit. On one hand, it ensures that technical limitations don't prevent legitimate customers from completing verification. On the other, it maintains strong security against sophisticated fraud attempts. The combination of AI-powered detection and human analysis provides comprehensive coverage across various scenarios and device capabilities.
Types of liveness detection
Two main approaches to liveness detection offer different advantages for user experience and security requirements.
Passive liveness detection provides a seamless user experience by requiring no specific actions from customers. "The user doesn't have to do anything,” explains Correa. “AI analyses a selfie or video for depth, skin texture, light reflection, and facial motion.” This approach detects fake sources like printed photos or screen replays through sophisticated analysis of natural biological characteristics.
Active liveness detection requires user participation through specific challenge-response tasks. "The user performs a simple task, such as blinking, turn their head, or speaking. The system then verifies their real-time responses and natural movement," says Correa. This method provides stronger security than passive liveness detection, as it ensures users can respond to real-time instructions that recorded content can’t replicate.
The choice between approaches generally depends on a variety of factors, such as client requirements and risk tolerance.
The role of liveness detection in customer onboarding
Ideally, liveness detection integrates seamlessly into digital onboarding flows. When it’s done right, it shouldn’t significantly impact conversion rates or the customer’s experience.
Clear instructions and intuitive interfaces help customers understand requirements, though some may need multiple attempts. "Some clients can't understand the instructions, so they may apply multiple times until they figure out what they need to do," Correa observes. This is a potential accessibility issue, but it can be mitigated by a system that provides clear, simple instructions.
The “difficulty” of a liveness detection challenge depends on the individual case. Different business partners may implement varying levels of strictness based on their risk tolerance and regulatory requirements. Some require specific movement patterns, whilst others accept general signs of life.
This flexibility allows organisations to balance security requirements with user experience considerations based on their individual needs and customer demographics.
Preventing replay attacks and fraud
Liveness detection can be a key defence against replay attacks. These attacks involve criminals using previously recorded legitimate verification videos to create multiple accounts or to access existing accounts fraudulently.
Active liveness detection technology is especially helpful in preventing these attacks, as it requires fresh, real-time interaction that can't be replicated through pre-recorded content. Because challenge-response tasks vary between sessions, it becomes much more difficult for fraudsters to prepare appropriate recorded responses in advance.
The role of human analysts
Despite advanced AI capabilities, human analysts play crucial roles in liveness detection, particularly for complex cases and fraud detection that automated systems might miss.
“Our main role as analysts is to detect fraud that an AI system may flag or miss,” says Correa. Human reviewers can generally identify subtle indicators that AI systems might not catch, particularly when fraudsters use sophisticated techniques that closely mimic legitimate behaviour.
Analysts undergo extensive training to recognise fraud patterns and authentic verification attempts. This expertise becomes particularly valuable when dealing with edge cases, poor video quality, or sophisticated fraud attempts that require contextual understanding beyond automated analysis capabilities.
The combination of automated processing for routine cases and human expertise for complex situations provides optimal security whilst maintaining efficient processing speeds. "A majority of onboarding cases can be processed solely by AI," notes Correa, allowing human analysts to focus on cases requiring detailed investigation.
Implementation considerations
Successful liveness detection implementation requires careful consideration of technical and operational factors.
User guidance plays a critical role in successful implementation. Clear instructions and intuitive interfaces help customers complete verification successfully whilst reducing support requirements and abandoned applications.
Quality assurance processes ensure consistent application of liveness detection standards across different analysts and review scenarios. Regular training and performance monitoring maintain detection accuracy whilst preventing false positives that could reject legitimate customers.
Technology integration must account for varying device capabilities and network conditions that customers may experience. Robust systems accommodate different smartphone models and connection speeds whilst maintaining security standards.
Flexible detection requirements allow organisations to adjust liveness detection strictness based on risk assessments and customer demographics whilst maintaining appropriate security levels for more sensitive cases that may require Enhanced Due Diligence (EDD), such as politically exposed persons (PEPs) and those appearing on sanctions lists.
All in all, modern liveness detection technology, when properly implemented with identity verification platforms like Fourthline, provides robust protection against fraud whilst enabling efficient digital onboarding.
Liveness detection FAQs
Does liveness detection work with all smartphone cameras?
Yes, modern liveness detection systems work with standard smartphone cameras, though performance may vary based on camera quality and lighting conditions. Systems include guidance to help users optimise their environment for successful verification.
How long does liveness detection typically take during onboarding?
Liveness detection usually adds only seconds to the verification process when completed successfully. While some users may need multiple attempts before the instructions are clear, the process remains quick and straightforward for most customers.
Can liveness detection be fooled by sophisticated deepfakes or masks?
Advanced liveness detection systems include specific protections against deepfakes and mask attacks through analysis of multiple biological indicators and behavioural patterns that are difficult to replicate artificially. Human analyst review provides additional protection for sophisticated fraud attempts.
This article incorporates insights from Yessica Correa, a Daily Operations Analyst at Fourthline. It is for informational purposes only and does not constitute legal advice.
· Jul 18, 2025