Prof. Josh P Davis
Face and Voice Recognition Lab
School of Human Sciences
Institute of Lifecourse Development
University of Greenwich
London SE10 9LS
www.superrecognisers.com
super-recognisers@greenwich.ac.uk
12 February 2021
Greenwich University help Frankfurt Police ID super-recognisers
A lockdown-delayed project wound up in December 2020, as Frankfurt Police in Germany announced they were starting to deploy super-recognisers identified using modified University of Greenwich testing methods (e.g., see Davis, 2019). The process is conducted in two phases. The first phase is online, and the highest scorers receive an invite to take part in more intense examination session. Normally, the final exam sessions are conducted with research assistants monitoring proceedings while present in the room. However, this time, the assistants monitored using a live video link, and assessed progress on the tests remotely. The full set of tests measured short-term face memory, long-term face memory, simultaneous face matching, and spotting faces in a crowd.
From this media report in Frankfurter Allgemeine, it appears that they are first to be deployed monitoring protests due to the construction of the A49 in the Dannenröder forest. Previously, Munich Police super-recognisers, selected using similar University of Greenwich methods, had assisted Frankfurt Police. This requires different skills to the typical CCTV review work undertaken by super-recognisers. That might be normally be completed in an office.
We have a fun 5-minute “Could you be a super-recogniser test” here that anyone can have a go at. It leads to more reliable tests and the opportunity to contribute to research.
Reference
Davis, J. P. (2019). The worldwide impact of identifying super-recognisers in police and business. The Cognitive Psychology Bulletin; Journal of the British Psychological Society: Cognitive Section, 4, 17-22. ISSN: 2397-2653.https://shop.bps.org.uk/the-cognitive-psychology-bulletin-issue-4-spring-2019
(free to download pre-print: https://doi.org/10.31234/osf.io/2ybau)
