RealNetworks Appoints Jay Burrell as Chief Revenue Officer for Computer Vision
May 01 2019 - 9:00AM
RealNetworks, Inc. (NASDAQ: RNWK), a leader in digital media
software and services, today announced the appointment of Jay
Burrell to the position of chief revenue officer (CRO) for computer
vision. Burrell will bring more than 20 years of leadership
experience at esteemed companies, including executive roles at GE,
IBM, and Microsoft. In this new role, Burrell will lead worldwide
sales and business development for RealNetworks’ facial recognition
platform, SAFR, reporting to Max Pellegrini, Real’s President.
“We are thrilled to have Jay join us to help take our SAFR
business to the next level,” said Rob Glaser, Founder and CEO of
RealNetworks. “Jay brings deep experience from his work at
well-respected and high-growth businesses. Further, he embodies the
drive, focus and diligence required to make SAFR a market leading
business that scales rapidly.”
“The facial recognition market is growing at an exponential
rate, with a variety of uses cases for businesses and individuals
alike,” said Burrell. “SAFR has already shown real market
leadership with its performance and accuracy, but this is just the
beginning. I have been following RealNetworks for some time, and
the opportunity to be part of the recreation of an industry icon is
exactly what a technology executive dreams of. I look forward to
working with the incredibly innovative SAFR team to build on our
momentum and grow into new markets and regions.”
Prior to joining RealNetworks, Burrell served as the senior vice
president and general manager of digital technology at GE
Healthcare. In this role, he launched the digital platforms within
one of the world’s largest healthcare companies. Before that, he
oversaw IBM Cloud as global vice president, where he was
instrumental in the initial stage of IBM establishing itself as one
the leading players in global cloud infrastructure and
services.
SAFR is an AI-powered facial recognition solution, optimized for
live video and architected to economically scale with high
performance and rapid processing to detect and match millions of
faces in real time. SAFR’s 99.86% accuracy rate is balanced by
industry-leading performance that delivers results 3-5 times as
fast as competing facial recognition algorithms. In April 2019,
published results from a National Institute of Standards and
Technology (NIST) test found the SAFR algorithm tested as both the
fastest and most compact amongst algorithms for wild images –
faces in motion, under poor lighting conditions, misaligned, or
partially obscured – with less than 0.025 FNMR. SAFR supports
flexible deployment – including on-premises, cloud or hybrid – and
is adaptable to a variety of use cases, including secure access,
multi-factor authentication, liveness detection, door locks,
lighting, analytics, reports, and more.
About RealNetworksBuilding on a legacy of
digital media expertise and innovation, RealNetworks has created a
new generation of products that employ best-in-class artificial
intelligence and machine learning to enhance and secure our daily
lives. SAFR (www.safr.com) is the world’s premier facial
recognition platform for live video. Leading in real world
performance and accuracy as evidenced in testing by NIST, SAFR
enables new applications for security, convenience, and analytics.
Kontxt (www.kontxt.com) is the foremost platform for categorizing
A2P messages to help mobile carriers build customer loyalty and
drive new revenue through text message classification and antispam.
For information about our other products visit
www.realnetworks.com.
Results shown from NIST do not constitute an endorsement of any
particular system, product, service, or company by
NIST. https://www.nist.gov/programs-projects/face-recognition-vendor-test-frvt-ongoing.
Media ContactLauren BogoshianOffleash for
RealNetworksP: 510-394-2145E: public_relations@realnetworks.com
A photo accompanying this announcement is available at
http://www.globenewswire.com/NewsRoom/AttachmentNg/8e8c9fa2-5cf1-44bd-8203-6e2fdccd1b2e.
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