NIST study shows face recognition experts perform better with AI
29 May 2018 10:37 GMT

A study to compare performances of trained facial examiners, super-recognisers, and facial-recognition algorithms, has revealed a combination of human and computer decision-making is most accurate.

The study, by a team of scientists from the National Institute of Standards and Technology in the US and three universities including UNSW Sydney, is published in the Proceedings of the National Academy of Sciences.

“Experts in face identification often play a crucial role in criminal cases,” says study team member, UNSW psychologist Dr David White, in a blog post on the university's website.

“Deciding whether two images are of the same person, or two different people, can have profound consequences.

“When facial comparison evidence is presented in court, it can determine the outcome of a criminal trial. Errors on these decisions can potentially set a guilty person free, or wrongly convict an innocent person,” he says.

The international study involved a total of 184 participants from five continents - a large number for an experiment of this type. 

Eighty-seven were trained professional facial examiners, while 13 were super-recognisers - people with exceptional natural ability, but no training. The remaining 84 were control participants with no special training or natural ability, including 53 fingerprint examiners and 31 undergraduate students.

Participants received pairs of face images and rated the likelihood of each pair being the same person on a seven-point scale. The research team intentionally selected extremely challenging pairs, using images taken with limited control of illumination, expression and appearance.

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