Face Liveness Check: A Comprehensive Method Comparison and Benchmarks

As fraudsters get smarter, businesses can’t rely on face recognition alone. The threats of 2024 and 2025 look very different from just a few years ago.

• Generative AI deepfakes can now mimic a person’s expressions in real time, tricking systems that once felt secure.
• Video call scams are rising, with fraudsters joining verification calls using AI-powered avatars of victims.
• Synthetic media (entirely AI-generated faces and voices) makes it easier than ever to create fake identities.

That’s why liveness detection has become the frontline defense for banks, fintechs, telecoms, and digital platforms. It ensures that the person in front of the camera is real, live, and present.

What is a Liveness Check?

Liveness check refers to the process of verifying whether a biometric sample, most often a selfie or video of your face, comes from a real, live person sitting in front of the camera, and not from a printed photo, a mask, an AI-generated deepfake or a synthetic identity.

Think of it as the “proof of life” step in any biometric journey. Without it, a photograph of someone else could unlock your account just as easily as the actual person.

Liveness detection involves the use of advanced algorithms to detect the dynamic qualities of the biometric data, such as facial expressions or pulse in the case of fingerprint scans. This is essential for enhancing the security of biometric authentication systems, as it guards against potential threats like spoofing or the use of non-viable samples.

Read more: A complete guide to facial recognition

Why is Liveness Detection Needed?

Today, face recognition is everywhere—digital onboarding, payments, eKYC, SIM activation, even workplace access. But it’s not foolproof. Fraudsters can and do trick systems using high-quality displays, printed masks, or even AI-generated faces.

Spoofing is a technique used by fraudsters where they mimic the face of the victim using instruments like high-quality displays, printed photos, printed masks with one or more layers, and a host of other techniques.

These techniques are becoming increasingly common due to easy access to tools such as photo editors, high-quality displays, and printers. Any face-based biometric authentication system must protect itself from fraudsters by using a robust liveness detection system.

Without liveness, a photo works just as well as a face. That’s why regulators, banks, fintechs, and telecoms insist on robust liveness checks: they protect both the business and the end-user.

How Does Liveness Detection Work?

Liveness detection uses multiple technical methods to spot real humans:

• Motion Analysis: Detects natural head and eye movements. A static image can’t replicate subtle micro-motions.
• 3D Depth Sensing: Uses infrared or structured light to capture 3D facial depth, making it nearly impossible for 2D photos or videos to pass.
• Texture Analysis: Examines skin reflectance, patterns, and micro-details that masks and screens fail to replicate.
• Challenge–Response Tests: Prompts users to blink, smile, or turn their head, proving responsiveness in real time.
• ML-Based Checks: AI models trained on millions of spoof attempts learn to spot anomalies in lighting, background consistency, or pixel-level artifacts unique to deepfakes.

There are three broad methods of liveness detection:

Active Liveness Detection

Here, the system asks the user to perform an action: blink, turn your head, read a phrase aloud. The idea is that a fake photo or screen can’t respond to instructions. The system can:

1. Ask the user to speak out the text displayed on the screen
2. Ask the user to hold a piece of paper where they write down some verification text
3. Ask the user to perform gestures such as moving their head up and down, left and right, blinking, etc.

Passive Liveness Detection

This is more user-friendly. Instead of asking the user to do anything, the system analyzes a selfie or short video for signals of life.

Passive detection systems typically use a fixed-length video capture of the user, which is then analyzed for properties such as light, skin texture, micro-motions, and other characteristics to determine if a live person is present in the capture.

Single Image Passive Liveness Detection

The latest and most frictionless approach: a single selfie (the same one used for face matching) is enough to verify liveness. This dramatically reduces drop-offs and speeds up onboarding.

Single image passive liveness detection makes user authentication very simple while ensuring you are protected against any spoofing attempts. In the table below, we’ve compared the liveness detection techniques on all attributes.

Single-image passive liveness delivers the lowest drop-off rates because it requires no extra user effort, adds zero latency to the journey, performs reliably even in poor network conditions, and can be implemented with minimal developer effort.

Attacks Prevented by Liveness

So, what kinds of fraud does liveness actually stop? Let’s break it down:

• Print Attacks: A fraudster waves a printed photo at the camera. Liveness spots the flatness, lack of texture, and absence of depth.
• Screen Attacks: The attacker uses a phone or laptop screen to display a face. Liveness detects glare, pixel patterns, and unnatural color distortions.
• Mask Attacks (2D/3D): Whether it’s a simple cut-out mask or a high-end silicone replica, liveness checks for depth, texture, and elasticity that masks simply can’t fake.
• Deepfakes & AI-Generated Video: The newest threat. Hyper-realistic, AI-generated video can mimic expressions. But liveness detects anomalies: micro-expressions, blinking patterns, light reflections, even pulse signals that deepfakes can’t replicate consistently.

Bottom line: liveness is the wall between fraudsters and your customer journey.

Liveness Detection & Compliance

Beyond security, liveness detection is becoming a compliance requirement across industries:

• ISO/IEC 30107-3: The global gold standard for Presentation Attack Detection (PAD). Solutions tested against it prove they can withstand spoofing attempts.
• RBI KYC Norms in India: For banks, NBFCs, and fintechs, liveness is essential to meet RBI’s video-KYC and digital onboarding guidelines. Without it, your KYC flow isn’t regulator-ready.
• DPDP Act, 2023: India’s new data protection law treats biometric data as sensitive. That means explicit consent, strict storage rules, and clear purpose limitation. Liveness solutions must comply.
• Global Privacy Laws: If you’re onboarding international customers, GDPR (Europe) and other regimes also apply. Transparency, audits, and secure handling of biometric data are no longer optional.

In short: liveness isn’t just smart, it’s legally necessary.

Liveness Check in Customer Onboarding

In industries like lending, insurance, securities, and telecom, liveness detection is now a non-negotiable part of digital KYC. However, subjecting customers to additional verification steps adds friction to the onboarding process and leads to high drop-off rates.

The best systems (like HyperVerge’s single-image passive liveness) are invisible to the user:

One Selfie = Face Match + Liveness Check

For checking a person’s identity, the user is asked to capture a selfie of themselves to match against their identity card. HyperVerge’s single-image liveness check uses this same selfie for liveness detection.

Immediate Feedback and Retries

In case a user captures a non-compliant selfie for any reason, the workflow should provide immediate feedback to the user and ask for a re-capture. Proper and immediate feedback ensures the least number of retries for the user, and that onboarding does not require manual verification of images, leading to a high turnaround time for completing onboarding.

HyperVerge’s single image comes with an array of compliance checks that ensure that immediate and contextual feedback is given, and the user can capture a correct image in the next attempt.

Low drop-offs, near-zero latency. That’s how top BFSI and telecom leaders in India scale secure onboarding without sacrificing user experience.

Liveness Check Benchmarks

Active liveness systems have traditionally achieved very high accuracy because fraudsters must overcome difficult challenges. For example, someone armed with only a digital photograph of the victim would struggle to pass a blink challenge.

Any passive liveness system that replaces an active one must therefore deliver the same level of performance. While passive systems bring many benefits—speed, convenience, and better user experience—they cannot compromise on security. They must be able to block all common spoofing attacks and maintain the lowest possible false-positive rates so that genuine customers face no friction during onboarding.

The problem with active systems is that, despite being secure, they are clunky—often causing drop-off rates as high as 50%. Video-based passive systems improve the experience, but still add 20–30 seconds of latency to the journey.

HyperVerge’s single-image passive liveness combines the best of both worlds: the security of active systems with the speed and ease of passive ones. Benchmarks show:

Liveness Detection	Accuracy Benchmarks
Live detected as live (True Positive Rate)	99.2%
Live detected as non-live (False Rejection Rate)	0.8%
Non-live detected as live (False Acceptance Rate)	0.2%
Non-live detected as non-live (True Negative Rate)	99.8%

Emerging Trends in Liveness Detection

The fraud landscape keeps evolving, and so does liveness. Here’s what’s next:

1. AI & ML-Powered Fraud Detection: Smarter models that spot deepfake artifacts, micro-expressions, even subtle physiological cues like pulse.
2. Cloud + Edge Deployment: Processing part of the liveness check on the device itself reduces latency, saves bandwidth, and works better in low-network areas (critical for India).
3. Multimodal Biometrics: Combining face + voice + behaviour = stronger, layered protection. Even if one modality is spoofed, others provide backup.
4. Real-Time Deepfake Detection: As generative AI improves, liveness must evolve to detect manipulation on the fly. Expect real-time detection to become standard.
5. Regulatory Push: RBI, UIDAI, and SEBI are expected to tighten compliance around biometric onboarding. Being certified against PAD standards will soon be table stakes.

Final Word

Fraudsters may try digital photos, printouts, or even masks, our liveness detection stops them all.

Why single-image liveness is the future:

• Lowest drop-off rates
• Works even in poor network areas
• Cuts manual costs
• Instant turnaround

All without compromising security.

That’s why the biggest names in telecom (Reliance Jio, Vodafone), lending (Aditya Birla Capital, L&T Financial, EarlySalary), securities (ICICI Securities, Angel Broking, Groww), payments (Razorpay), and e-commerce (Swiggy) already trust HyperVerge to onboard millions safely.

👉 Check out our liveness detection solution or contact us here.

FAQs on Liveness Detection

Q1. Can liveness detection be spoofed?
No system is 100% foolproof, but ISO-compliant solutions significantly reduce risk. Regular updates keep them ahead of fraudsters.

Q2. Does liveness make onboarding harder?
Not with single image passive liveness. It runs in the background of a selfie, adding zero extra steps.

Q3. What industries need liveness in India?
Banking, lending, insurance, telecom, securities, payments, gaming, and even e-commerce are now adopting it.

Q4. Is biometric data stored permanently?
No. Under DPDP Act and global privacy laws, biometric data must only be used with consent and stored securely for the minimum required time.

Q5. How does HyperVerge’s liveness compare globally?
With 99.8% accuracy, low false positives, and <1% drop-offs, it outperforms industry averages while remaining regulator-compliant.