Pinn to integrate Ever's facial recognition technology into secure attribution platform

REDWOOD CITY, Calif., July 13, 2017 /PRNewswire/ -- Ever and Pinn today announced a technology partnership to integrate Ever's industry-leading facial recognition into Pinn's secure attribution platform. The partnership will enhance Pinn's continuous attribution capabilities across devices and networks.

Pinn's attribution platform uses the recognition and analysis of an activity that users do frequently – like how they hold their smartphone, how they move their mouse, even how they drive their car – as a new layer of authentication. Pinn collects this data from a wide variety of locations to create a singular and seamless form of identification.

"The greatest systemic risk to business conducted over the internet is the username and password scheme," said Will Summerlin, Chief Executive Officer, Pinn. "It provides no real assurance in identity of a human. Combining Pinn's attribution platform and Ever's facial recognition technology, we're able to provide an unprecedented level of assurance in the identity of a human user. This advancement will drive security forward across critical infrastructure, government and enterprise."

Ever's facial recognition models are trained on the billions of photos the company stores for its consumer-oriented service.  The models will be available via a hosted API or self-hosted API with pricing competitive with industry peers.  An industry first, the models will also be available via client-side SDK in a sub-20MB package, making it easy for developers everywhere to integrate facial recognition into their applications with a high degree of performance, accuracy, security, and at a large cost advantage over server side models.

Artificial intelligence, the technology powering both Pinn and Ever, uses machine learning to recognize patterns from structured and unstructured data. Ever hosts billions of photos captured in a variety of angles and lighting situations. The quantity and range of hosted photos allows Ever to train machine learning models to be some of the most accurate in the world. The accuracy of Ever's models will significantly boost Pinn's ability to efficiently attribute identity across any sensor platform.

"The anonymity of users in secure environments is a problem that's not going away," said Doug Aley, Chief Revenue Officer, Ever. "It's time to find better, more accurate means of attributing identity. Pinn has led the charge in attribution and by combining their capabilities with our facial recognition technology, we're able to provide an incredibly high level of assurance in the human identity of a user."

Security is the first beneficiary of continuous attribution but certainly not the last. Ever's facial recognition expands Pinn's ability to attribute human identity in real time for advertisers across a variety of environments.

"Traditionally, online ads have been attributed to a device, not a human," said Summerlin. "This practice has left advertisers with limited visibility into impression measurement and overall ROI. Pinn's continuous attribution and Ever's facial recognition enables platforms and advertisers to attribute the identity of a human viewing an ad, improving visibility and increasing the value of each impression."

About Pinn:

Pinn is a software company dedicated to solving the hardest problems governments, financial institutions, and internet companies face in attributing identity. Our secure attribution platform closes the security gap between humans and machines - enabling continuous attribution and providing trust in secure transactions.

About Ever:

Ever is a San Francisco-based software company backed by Khosla, Icon, Felicis, and SV Angel, among others.  The company has one of the most popular consumer photo storage and organization services globally, storing billions of photos for tens of millions of users across 95 countries.  The release of its facial recognition and face clustering services is in direct response to increasing demand from enterprise developers.

SOURCE Pinn