Facial recognition’s failings: Coping with uncertainty in the age of machine learning
Deep learning is a technology with a lot of promise: helping computers “see” the world, understand speech, and make sense of language.
But away from the headlines about computers challenging humans at everything from spotting faces in a crowd to transcribing speech — real-world performance has been more mixed.
One deep-learning technology whose real-world results have often disappointed has been facial-recognition.
In the UK, police in Cardiff and London used facial-recognition systems on multiple occasions in 2017 to flag persons of interest captured on video at major events. Unfortunately, more than 90% of people picked out by these systems were false matches.
The shortcomings of publicly available facial-recognition systems were further highlighted in summer this year, when the American Civil Liberties Union (ACLU) tested the AWS Reckognition service. The test found that 28 members of the US Congress were falsely matched with mug shots from publicly available arrest photos.
Professor Chris Bishop, director of Microsoft’s Research Lab in Cambridge, said that as machine learning technologies were deployed in different real-world locales for the first time it was inevitable there would be complications.
“When you apply something in the real world, the statistical distribution of the data probably isn’t quite the same as you had in the laboratory,” he said.
“When you take data in the real world, point a camera down the street and so on, the lighting may be different, the environment may be different, so the performance can degrade for that reason.
“When you’re applying [these technologies] in the real world all these other things start to matter.”
Deep learning relies on training deep neural networks — mathematical models loosely inspired by the structure of the brain — teaching these networks to make accurate predictions, typically by feeding them huge amounts of labelled data.
In facial-recognition systems, accuracy can suffer when the images the system has been trained on aren’t sufficiently varied — in terms of factors like the individuals’ pose, lighting, shadows, obstructions, glasses, facial hair, and the resolution of the image.
“The learning process allows the machine to be robust to the variability that is well represented in the training material, but not to the variability that is not represented,” said Alessandro Vinciarelli, professor in the school of computing science at the University of Glasgow.
The need to cope with the extreme variability and messiness of the real world makes training facial-recognition systems for use in public far more demanding, said Professor Mark Nixon, president of the IEE Biometrics Council and professor in Computer Vision at the University of Southampton.
“There are a lot of variables which conflate the recognition problem, so the current machine learning approaches would need a database of impractical size,” he said.
Along with increased complexity, real-world deployments of machine-learning systems have to be able to resist attempts to trick them. In the case of facial-recognition systems, examples of such attacks include printing a pattern onto glasses that disrupts the system’s ability to recognise faces, resulting in a facial-recognition system failing 80% of the time in one instance.
“There are a lot of bad actors in the world and you have to be bulletproof against adversaries,” said Bishop.
Another problem stemming from training data not being sufficiently varied is bias.
