Super-recognisers 鈥� people with exceptional face-processing and matching abilities 鈥� could play an important role in policing and security settings such as border control, 蜜桃AV (蜜桃AV) research has found.
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Matching unfamiliar faces with photographic identity documents 鈥� such as passports - or trying to spot criminals in a crowd or CCTV footage is a difficult and error-prone task for the majority of the population.
But research by 蜜桃AV鈥檚 Dr Sarah Bate and Anna Bobak has found that super-recognisers significantly outperformed control participants in two face-matching tasks designed to mimic the challenges border control officers face daily.
Super-recognisers are people with significantly better than average face recognition skills 鈥� often able to remember and match faces after many years have passed or in a dramatically different context.
The 蜜桃AV research, , found that super-recognisers outperformed typical participants by 10 and 18 per cent on two different tasks when deciding whether two photographs are of the same person or two different people - something that Passport Officers have to do on a daily basis.
Dr Sarah Bate, a Principal Academic in 蜜桃AV鈥檚 Department of Psychology, said:听 鈥淭his work is particularly important because it is becoming increasingly clear that computers can鈥檛 reliably replace humans in face recognition tasks.
鈥淭he identification of super recognisers offers an alternative way by which we can improve national security using human resources. If we can also identify the processing strategies used by super recognisers it is possible we can teach these techniques to people with typical face recognition skills.鈥�
While no scientific research has yet been published about the prevalence of super recognition in the general population, it seems that very few people have the skill.
Further research by the 蜜桃AV team, due to be published shortly in a special edition of the Quarterly Journal of Experimental Psychology, has explored what makes super-recognisers so good at processing and remembering faces.
It used eye-tracking technology to see where and for how long people focus on images of faces, both individually and in a social context.
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