Picking faces out of a crowd is something humans are hardwired to do, but training computers to act in the same way is much more difficult.
There have been various breakthroughs in this field in recent months, but the latest could be the most significant yet.
Researchers from Yahoo Labs and Stanford University have developed an algorithm that can identify faces from various different angles, when part of the face is hidden and even upside down.
At the moment, the so-called Deep Dense Face Detector doesn’t recognise who the individual faces belong to, just that there is a face.
But the technology has the potential to be trained in this way.
The algorithm was built by Sachin Farfade and Mohammad Saberian at Yahoo Labs in California and Li-Jia Li at Stanford University.
It built on the Viola-Jones algorithm which spots front-facing people in images by picking out key facial features such as a vertical nose and shadows around the eyes.
By collecting these markers, the algorithm is able to determine if an image contains a face or not.
But, this did not account for faces that have been obscured, are looking in various directions, or were upside down.
With this in mind, Mr Farfade and his team used a form of machine learning known as a deep convolutional neural network.
This involves training a computer to recognise elements of images from a database using various layers.
Google used a similar technique for its recent GoogLeNet classification algorithm that can identify images within images, such as a hat on the head of a dog sat on a bench.
Mr Farfade trained his algorithm using a database of 200,000 images featuring faces shown at various angles and orientations, plus 20 million images that didn’t contain faces.
In their paper, the researchers said: ‘In this paper we propose a method based on deep learning, called Deep Dense Face Detector.’
‘It has minimal complexity…and can get similar or better performance [than other systems] while it does not require annotation or information about facial landmarks.’
And, the team said the technology could be improved following further training.
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