The use of facial recognition for authentication is becoming increasingly prevalent — especially on mobile devices. But between easy access to images on social media and advances in digital and print image resolution, biometric systems have security gaps that fraudsters can exploit to successfully spoof a facial recognition system.
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What is Facial Liveness Detection?
Facial liveness has emerged as a way to stop fraud and ensure the integrity of face biometrics as a means of authentication. Whereas face recognition for authentication can accurately answer the question “Is this the right person?” it doesn’t answer the question, “Is this a real person?”. This is the role of liveness detection.
Facial liveness detection works with a biometric system to measure and analyze physical characteristics and reactions in order to determine if a biometric sample is being captured from a living subject who is present at the point of capture.
IDLive Face: The New Generation of Liveness Detection
IDLive Face is the world’s first passive facial liveness detection product to identify spoofing attempts based on the same selfie used for facial matching and NO user participation. This unique single image approach that is fast, accurate, and doesn’t require any capture-side software. The face verification product works in the background, making it completely frictionless for the user while preventing fraudsters from knowing when it’s happening.
Make the switch from Active to Passive Liveness
Understanding passive vs. active facial liveness is crucial. Most of today’s facial liveness technologies are “active”, requiring users to blink, turning their heads or move their phone back and forth. This results in three issues: First, fraudsters can present a photo with cut out with eye holes, use a mask or show a video to trick the system. Second, challenge-response techniques put attackers on alert that they are being checked. And lastly, active methods create friction that slows the authentication process, increases abandon rates and diminishes the overall user experience.
The team at ID R&D has worked relentlessly to ensure you don’t have to sacrifice usability for security. Customers and partners who have made the switch from active to passive, report significant reduction in abandonment, lower false rejections of real users, and highly accurate presentation attack detection.
IDLive Face is the only single frame passive liveness product to achieve Levels 1 and 2 ISO/IEC 30107-3 PAD compliance with a perfect score.
Read more here.
IDLive Face Features
Passive UX – no action required- No special capture software required
- Single image analysis (vs multiple frames or video)
- Background implicit method of attack detection
- Identification of natural face movements (when available)
- Level 1 and 2 ISO 30107-3 compliance; tested with both iOS and Android
- Cross channel input: mobile, web, stand-alone devices
IDLive Face Benefits
- Strengthen the security of mobile and web authentication
- Eliminate friction by working in the background with no active participation from the user
- Prevents fraudsters from knowing liveness detection is occurring
- Ease remote customer onboarding and improve identity proofing processes
- Reduce fraudulent accounts and account takeovers
- Generates no incremental traffic to the server, making it ideal for customers operating in countries where bandwidth is scarce or expensive.
Use Cases
ID R&D passive liveness is the choice of leading identity and access management vendors, onboarding and KYC providers, and enterprise customers worldwide. Our solution is deployed in financial institutions, governments, healthcare organizations, standalone access systems, and more.
Passive Facial Liveness Explained
See how Passive Facial Liveness works
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How to Reduce Friction and Fraud in Digital Onboarding with Passive Facial Liveness.
Face Recognition is frequently used to match the image on a user’s government issued ID to a selfie. Liveness detection is equally crucial to ensure fraudsters cannot use high resolution print outs, masks or video to spoof the process. This paper dives deeper into the technology, including passive vs active approaches and considerations for choosing the right solution for your use case. Read it here.