` Photographs of your Friends and Neighbours on `Facebook ‘ could now be Instantly Recognisable ‘

#AceSecurity News – ‘DeepFace’ could provide instantaneous facial recognition via Facebook.

Deep FaceDeepFace: Closing the Gap to Human-Level Performance in Face Verification


In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4,000 identities, where each identity has an average of over a thousand samples. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.25% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 25%, closely approaching human-level performance.

Along with owning the largest stockpile of photos in the world, Facebook has announced it now plans to unleash facial recognition technology with a new program that promises to identify the subject of an untagged image with nearly unparalleled accuracy.

Researchers at the social media giant claim that humans who look at two faces can identify if they are the same person with a 97.53 percent accuracy.

They promise that the company’s new “DeepFace” program will be able to do the same with 97.25 percent accuracy.

Facebook users may have already noticed that the site is able to suggest friends to tag when a new picture is uploaded.

It does so by analyzing the distance between an individual’s eyes and nose in both profile pictures and already tagged images.

The new DeepFace program will be much more intensive, using software to correct the angle of a face in an image, then comparing that to a 3D model of an average face. It then simulates what has been called a neural network to find a numerical description of the face. If there are enough similarities, Facebook will know if the faces are in fact the same.

DeepFace was developed by Facebook artificial intelligence (AI) analysts Yaniv Taigman, Ming Yang, and Marc’ Aurelioa Ranzato, along with Lior Wolf, a faculty member at Tel Aviv University in Israel. Their research paper was first published last week in the Massachusetts Institute of Technology‘s Technology Review.


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