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Face averages and multiple images in a live matching task
Author(s) -
Ritchie Kay L.,
Mireku Michael O.,
Kramer Robin S. S.
Publication year - 2020
Publication title -
british journal of psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.536
H-Index - 92
eISSN - 2044-8295
pISSN - 0007-1269
DOI - 10.1111/bjop.12388
Subject(s) - matching (statistics) , face (sociological concept) , artificial intelligence , task (project management) , computer vision , context (archaeology) , image (mathematics) , computer science , psychology , facial recognition system , pattern recognition (psychology) , statistics , mathematics , geography , social science , management , archaeology , sociology , economics
We know from previous research that unfamiliar face matching (determining whether two simultaneously presented images show the same person or not) is very error‐prone. A small number of studies in laboratory settings have shown that the use of multiple images or a face average, rather than a single image, can improve face matching performance. Here, we tested 1,999 participants using four‐image arrays and face averages in two separate live matching tasks. Matching a single image to a live person resulted in numerous errors (79.9% accuracy across both experiments), and neither multiple images (82.4% accuracy) nor face averages (76.9% accuracy) improved performance. These results are important when considering possible alterations which could be made to photo‐ID. Although multiple images and face averages have produced measurable improvements in performance in recent laboratory studies, they do not produce benefits in a real‐world live face matching context.