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NIST recognizes NEC for ‘best-in-class’ facial recognition technology


NEC Corporation of America (NEC), a leading technology provider of strategic IT and communications solutions, has recently announced that its NeoFace product has achieved the highest rank in JPEG compression, JPEG compression with inter-eye distance reduction, and JPEG 2000 compression and JPEG 2000 compression with inter-eye distance reduction investigations.

The survey investigations were performed by the United States National Institute of Standards and Technology (NIST) and were released in NIST’s Interagency Report – NISTIR 7830 – Performance of Face Recognition Algorithms on Compressed Images.

NeoFace is a cutting – edge facial recognition technology, introduced by NEC, which delivers accurate biometric matching across a various range of applications.

In addition to NIST 2010 Multiple Biometric Evaluation (MBE), the latest tests performed with NEC NeoFace’s facial recognition innovation convincingly demonstrated superior performance with regards to extremely compressed images that ranges to as low as 2,000 bytes and inter-eye distance of as low as 24 pixels.

“NEC NeoFace technology’s strength lies in its tolerance of poor quality, highly compressed surveillance videos and images, previously considered of little to no value, leading to higher rate of positive identification,” says Raffie Beroukhim, Vice President of NEC’s Biometrics Solutions Division. “While searching of latent “scene of crime” fingerprints is the norm, NEC NeoFace facial recognition technology can now positively identify latent photos with high degree of accuracy.”

NEC has invested significant resources in biometric research and development since the 1970s. It has also consistently obtained recognition for world-leading results through independent, third-party testing. NEC’s facial recognition technologies have been globally utilized and adopted in most immigration systems, law enforcement applications, and amusement parks.

Will NEC’s NeoFace technology change the implementation of facial recognition?

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