
"They have been used in the search for the Salisbury novichok poisoners, finding murder suspects and even spotting sexual predators. Now, research has revealed fresh insights into why super-recognisers are so good at identifying faces. Previous research has suggested people with an extraordinary ability to recognise people look at more areas across a face than typical people. Now researchers have used a type of AI to reveal how this approach aids their prowess."
"In that work, participants were shown both pictures of full faces and ones where the area of the face they were looking at was made partly visible. In the new study, the team used this data to reconstruct the actual visual information seen by participants' eyes. This retinal information was then fed into deep neural networks (DNNs) a type of AI system that were trained to recognise faces."
"In each case the AI system produced a score for how similar the retinal information was to the full facial image it had been given. The team compared the results of typical participants and super-recognisers as well as data based on randomly selected areas of the initial facial image. The results reveal in all cases the performance of the AI system increased as the parts of the face being looked at were made more visible."
Eye-tracking data from 37 super-recognisers and 68 typical recognisers recorded viewing of full faces and partially visible regions. Reconstructed retinal inputs were fed into deep neural networks trained for face recognition alongside full-face images of same or different identities. The AI produced similarity scores between retinal inputs and full-face images. Performance improved as visibility of viewed regions increased. Across all visibility levels, AI performance was consistently higher when using retinal inputs from super-recognisers than typical recognisers or randomly sampled regions. Diagnostic, targeted sampling of facial features underlies superior face-recognition ability.
Read at www.theguardian.com
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