Perceptual expertise in forensic facial image comparison

Published

September 1, 2015

Author(s)

P. Jonathon Phillips, David White, Alice O'Toole, Carina A. Hahn, Matthew Hill

Abstract

Forensic facial identification examiners are required to match the identity of faces in images that vary substantially, owing to changes in viewing conditions and in a person's appearance. These identifications affect the course and out- come of criminal investigations and convictions. Despite calls for research on sources of human error in forensic examination, existing scientific knowledge of face matching accuracy is based, almost exclusively, on people without formal training. Here, we administered three challenging face matching tests to a group of forensic examiners with many years' experience of comparing face images for law enforcement and government agencies. Examiners outperformed untrained participants and computer algorithms, thereby providing the first evidence that these examiners are experts at this task. Notably, computationally fusing responses of multiple experts produced near-perfect performance. Results also revealed qualitative differences between expert and non-expert performance. First, examiners' superiority was greatest at longer exposure durations, suggestive of more entailed comparison in forensic examiners. Second, experts were less impaired by image inversion than non- expert students, contrasting with face memory studies that show larger face inversion effects in high performers. We conclude that expertise in matching identity across unfamiliar face images is supported by processes that differ qualitatively from those supporting memory for individual faces.

Citation

Proceedings of the Royal Society B-Biological Sciences

Volume

282

Pub Type

Journals

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Citation

Phillips, P. , White, D. , O'Toole, A. , Hahn, C. and Hill, M. (2015), Perceptual expertise in forensic facial image comparison, Proceedings of the Royal Society B-Biological Sciences, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=917560 (Accessed November 8, 2024)