RealNetworks Launches SAFR, a Best-In-Class Facial Recognition Platform
AI Matches Millions of Faces in Real Time
SAFR is distinguished from other facial recognition platforms in three ways:
- World class accuracy — SAFR recognizes faces with proven 99.8 percent accuracy for Labeled Faces in the Wild (LFW), based on the
University of Massachusettsbenchmark. In addition, the June results of RealNetworks'first submission to the National Institute of Standards and Technology(NIST), ranked SAFR's recognition algorithm for "Wild Faces" False Non-Match-Rate (FNMR), as the best of any platform submitted by a United States-based company and as one of the world's top recognition algorithms. NIST is the premier measurement standards laboratory for the United States.
- Extraordinary efficiency and flexibility — SAFR works seamlessly with existing IP-based cameras and readily available hardware to recognize people in real time, helping to enhance secure access and surface insights. SAFR supports both cloud and local storage. System integrators and application developers can easily integrate with the SAFR platform through RESTful API's, an SDK, and dashboard.
"The architectural design of SAFR takes a novel approach to facial recognition at scale," said
- Focus on privacy and socially positive use cases — SAFR encrypts all facial data and images to ensure privacy. When used locally, no personal or facial data is ever transmitted over the Internet. Moreover,
RealNetworksis announcing that SAFR is being made available for free to over 100,000 K-12 schools in the United Statesand Canada(see separate release).
"Privacy concerns are paramount in our design thinking," said
SAFR supports numerous secure access use cases where facial recognition can replace the use of an ID badge, securely automate entry to facilities, trigger notifications, and log events for analytics. SAFR provides facial detection and tracking of many faces in a single camera feed. Each face can be selectively analyzed for age, gender, sentiment, and liveness. Faces are rapidly matched to a database of enrolled faces, returning each identity with a recognition confidence score. SAFR works with off-the-shelf IP cameras and runs on mobile devices and readily available computers.
Building on its legacy and leadership in streaming media—including compression, media delivery, digital rights management, and efficient scalability—RealNetworks' expertise now extends to AI, machine learning and deep neural networks: the foundation of SAFR.
SAFR for K-12 is being offered for free to K-12 schools in
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