SAFR™ SCAN, the AI-powered face authentication solution designed for mainstream commercial applications, has achieved a flawless 0% success rate against presentation attacks in independent testing conducted by iBeta, the industry standard for anti-spoofing testing.
iBeta Quality Assurance conducted Presentation Attack Detection (PAD) testing in accordance with ISO/IEC 30107-3. Accredited by NIST/NVLAP (NVLAP Testing Lab Code: 200962), iBeta provides rigorous evaluation of biometric systems against spoofing attempts. A presentation attack occurs when an unauthorized individual attempts to gain access by presenting fake biometric data, such as photos, videos, or 3D masks, to a facial recognition system. SAFR SCAN yielded an overall Presentation Attack (PA) success rate of 0%, as well as an Imposter Attack Presentation Accept Rate (IAPAR) of 0% and a bona fide False Non-Match Rate (FNMR) of 0%.
“Presentation attacks are the single biggest security threat to face-based access control devices. Detecting sophisticated attempts to spoof these devices using both 3D and 2D liveness technologies is what differentiates SAFR™ SCAN from other devices,” said Brad Donaldson, VP of Computer Vision, SAFR from RealNetworks.
SAFR SCAN is a market-leading access control solution that combines advanced facial authentication with integrated access control, IP video, intercom functionality, and door station capabilities. Built for speed and security, SAFR SCAN authenticates up to 30 individuals per minute, delivering a fast, frictionless user experience.
About SAFR
SAFR from RealNetworks is the only unified facial recognition ecosystem built for the enterprise, seamlessly integrating access control, specialized edge-analytics cameras, mobile solutions, and PACS/VMS systems. Powered by AI-driven computer vision, SAFR enhances security while prioritizing privacy, accuracy, and fairness. Designed in the USA, SAFR builds on RealNetworks’ 30+ years legacy of innovation.
