Facebook’s ‘Deepface’ photo-matching
software can now ‘recognize’ pairs of human faces with an accuracy just a
fraction of a percentage point behind human beings – a huge leap
forward in the technology, which some see as having potentially alarming
implications for privacy.
Deepface can now match two previously unseen photos of the same face
with 97.25% accuracy – humans can do the same with around 97.5%
accuracy, a difference which TechCrunch describes as “pretty much on par”.
Facebook uses its current facial recognition software to
‘tag’ people in photos, which is used widely around the world. Although
Deepface is a research project, and unrelated to the technology used on
the site, it “closes the vast majority of the performance gap” with
human beings according to the Facebook researchers behind it (PDF research paper here), and can recognise people regardless of the orientation of their face, lighting conditions and image quality.
Publications such as Stuff magazine describe
the technology as “creepy”, saying that were it implemented “in the
wild” it should make site users “think twice” about posting images such
as “selfies.”
Deepface uses deep learning to leap ahead of current
technology – an area of AI which uses networks of simulated brain cells
to ‘recognize’ patterns in large datasets, according to MIT’s Technology Review.
Yaniv Taigman of Facebook’s AI team says, “You don’t normally see that sort of improvement. We closely approach human performance.”
The leap forward in performance cuts errors by more than 25% in the
accuracy – achieved, Taigman says in Facebook’s brief description of the
milestone, by 3D modeling faces, and using a “nine-layer deep neural
network” to analyze 120 million parameters. Business Insider describes the process as akin to using the 3D software to turn faces “forward” for comparison.
Deepface was “trained” using a dataset of four million facial images belonging to 4,000 individuals, Taigman says.
“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%,” Taigman says, noting that the software is
“Closely approaching human-level performance.”
In a paper entitled, Deepface: Closing the Gap to Human-Level Performance in Face Verification,
Taigman and his co-authors write, “We believe that this work, which
departs from the recent trend of using more features and employing a
more powerful metric learning technique, has addressed this challenge,
closing the vast majority of this performance gap [as compared with
humans],” saying that Deepface can be applied to various population,
without regard to pose illumination or image quality.
“Our work demonstrates that coupling a 3D model-based alignment with
large capacity feedforward models can effectively learn from many
examples to overcome the drawbacks and limitations of previous methods.”