TechCrunch pegs the acquisition price at $22 million.
Polar Rose specialises in facial recognition technology. It just recently decided to start licensing its technology.
Apple already uses some facial recognition technology in iPhoto. Perhaps it will expand that? Or maybe it will use the technology in the mobile area. Anybody have any ideas?
Here’s a description of two of Polar Rose’s specialties, as well as a video of the technology:
FaceCloud is Polar Rose’s flagship enterprise face recognition solution. FaceCloud is an end-to-end scalable server-side solution for adding face recognition to virtually any service.
Targeted to web-service providers, social networks, carriers and other companies with photo repositories, FaceCloud offers a full photo processing pipeline, high-level HTTP+JSON API, an embeddable user interface and complete identity & privacy management.
FaceCloud is also built from the ground up to support social networks. The use of social networks with FaceCloud leads to enhanced recognition performance and the ability to train against tagged photos already present on the social network.
FaceCloud’s data model has been designed to maximise face recognition efficiency and overall scalability. Its photo processing pipeline can even scale out to Amazon EC2 in case of usage-peaks.
FaceCloud supports most common online face recognition scenarios, such as naming everyone in a photo album or automatically tagging people in a photo. Together with FaceLib it can provide auto-tagging at point-of-capture.
Our show-case website http://www.polarrose.com was built using FaceCloud. It is available as a self-hosted solution or as a managed service.
FaceLib is Polar Rose’s high performance mobile face recognition solution, available as a shared library for Android or iPhone (other platforms on request).
It offers an API with high- and low-level integration options in each platforms native language. Depending on the customers needs it runs stand-alone on the phone or connects to FaceCloud in order to off-load processing for lower end phones. This also enables hybrid solutions, which run partially on-phone, partially on-server. The Recognizr concept application is an example of such a solution.
FaceLib offers full face detection, tracking of faces in video and on-device recognition. It also offers high-speed communication with FaceCloud, using a proprietary protocol, optimised for mobile use. FaceLib offers a multitude of options to tune the detection and recognition and is easy to fit in to any mobile app.
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