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Improving forensic perpetrator identification with Super-Recognizers.

Maren MayerMeike Ramon
Published in: Proceedings of the National Academy of Sciences of the United States of America (2023)
About a decade ago, Super-Recognizers (SRs) were first described as individuals with exceptional face identity processing abilities. Since then, various tests have been developed or adapted to assess individuals' abilities and identify SRs. The extant literature suggests that SRs may be beneficial in police tasks requiring individual identification. However, in reality, the performance of SRs has never been examined using authentic forensic material. This not only limits the external validity of test procedures used to identify SRs, but also claims concerning their deployment in policing. Here, we report the first-ever investigation of SRs' ability to identify perpetrators using authentic case material. We report the data of 73 SRs and 45 control participants. These include (a) performance on three challenging tests of face identity processing recommended by Ramon (2021) for SR identification; (b) performance for perpetrator identification using four CCTV sequences depicting five perpetrators and police line-ups created for criminal investigation purposes. Our findings demonstrate that the face identity processing tests used here are valid in measuring such abilities and identifying SRs. Moreover, SRs excel at perpetrator identification relative to control participants, with more correct perpetrator identifications, the better their performance across lab tests. These results provide external validity for the recently proposed diagnostic framework and its tests used for SR identification (Ramon, 2021). This study provides the first empirical evidence that SRs identified using these measures can be beneficial for forensic perpetrator identification. We discuss theoretical and practical implications for law enforcement, whose procedures can be improved via a human-centric approach centered around individuals with superior abilities.
Keyphrases
  • bioinformatics analysis
  • systematic review
  • machine learning
  • electronic health record
  • big data
  • working memory
  • artificial intelligence