AI

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5 min read

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April 28, 2021

Face Liveness Checks: Meaning, Type & Uses In Face Recognition

Face recognition is being increasingly adopted by the industry as a safe, secure, hassle-free method of onboarding, authenticating, and verifying customers. Customers gain access to services by authenticating their faces. Modern face recognition can classify whether two faces belong to the same person with very high accuracy.

Why high-quality single image liveness is the way forward for modern, fraud-resistant, customer-centric onboarding solutions

Face recognition is being increasingly adopted by the industry as a safe, secure, hassle-free method of onboarding, authenticating, and verifying customers. Customers gain access to services by authenticating their faces. Modern face recognition can classify whether two faces belong to the same person with very high accuracy.

However, these systems are susceptible to spoofing. 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 authentication system must protect itself from fraudsters by using a robust liveness detection system.

For a face-authentication system without liveness detection, a photograph or any display works just the same as the actual person!

Types of Liveness Detection Systems

Liveness detection systems are classified into two categories based on the input needed from the end-user:

Active Liveness Detection

This method requires the user to perform an action or a gesture to verify whether the user is physically present for verification. In the market, one can find systems that

  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 move their head up and down, left and right, blink, etc.

Passive Liveness Detection

This is an advanced technique that detects whether a user is physically present without requiring any explicit action or gesture. These 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.

A more advanced technique requires the capture of a single image, which is analyzed for an array of complex characteristics to determine if a live person is present.

Advantages of Single Image Passive Liveness Detection

Single image passive liveness detection is gaining traction among regulators and service providers alike, as it makes user authentication and onboarding very simple while ensuring that services are protected against spoof attempts. In the table below, we've compared the liveness detection techniques on all attributes.

Comparison of Liveness Detection Techniques
Single image passive liveness, by virtue of having no additional end-user effort, has the lowest drop-off rates, adds no latency to the user journey, and works well in poor network conditions, as well, with minimal developer effort in implementation.

Liveness Detection in Customer Onboarding

Companies involved in providing financial services such as lending, insurance, investments, wealth management, payments, and others must ensure they perform proper KYC of users before allowing access to services. In addition to it being a compliance requirement, proper identity checks ensure that the company is protected from fraudsters and scammers getting access to its services.

However, subjecting customers to additional verification steps adds to friction to the onboarding process and leads to high drop-off rates.

The ideal onboarding workflow verifies the user's identity and liveness with high confidence while adding little to no friction in the onboarding process.

An ideal passive liveness solution for customer onboarding should have most, if not all of the following characteristics.

Single Capture for both face-match and liveness

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 uses this same selfie to evaluate liveness.

Immediate Feedback and Retries

In case a user captures a non-compliant selfie due to any reason, the workflow should provide immediate feedback to the user, and ask for a re-capture. Proper and immediate feedback ensure the least number of retries for the user, and that onboarding does not require manual verification of images leading to a high turn-around 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 is able to capture a correct image in the next attempt.

Performance at par with any active liveness technique

Active liveness systems traditionally have performed at very high accuracy levels due to a fraudster having to pass difficult challenges. For instance, a fraudster with a digital photograph of the victim would find it very difficult to pass a challenge where the user is required to blink.

Any passive liveness system that replaces an active liveness system must provide on-par performance with traditional active liveness.

Passive liveness systems, while providing numerous benefits, must not compromise on performance and be capable of stopping all typical spoof attacks.

Additionally, passive liveness systems should have the lowest possible false-positive rates, to ensure genuine customers do not face any friction in the onboarding process.

HyperVerge's single image passive liveness system has been rigorously tested on industry-standard benchmarks and has provided ~99.8% accurate predictions in live environments.

HyperVerge Single Image Passive Liveness Benchmark

Additionally, our systems are reinforced for popular techniques used by fraudsters such as digital photographs, printed images, printed masks, etc to ensure maximum protection.

We firmly believe single image liveness is the best way forward for organizations looking to scale their business. The system provides

  • very low drop-off rates
  • serviceability in areas with low network coverage
  • low manual operations costs
  • very minimal turnaround time

All this comes at no compromise in protection against fraudsters!

HyperVerge has enabled large organizations to safely authenticate and/or onboard millions of users over the past decade with minimal onboarding effort and turnaround time while ensuring protection against any fraudulent activity.

Large customers in telecom (Reliance Jio, Vodafone, etc), lending (Aditya Birla Capital, L&T Financial, EarlySalary, etc), securities (ICICI Securities, Angel Broking, Groww, etc), payments (Razorpay), e-commerce (Swiggy), and other industries trust HyperVerge's onboarding solutions to safely onboard their users.

To speak to one of our solution experts, reach out to us at contact@hyperverge.co.

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