Forensic Science International: Reports (Jul 2022)
Face recognition in police officers: Who fits the bill?
Abstract
Accurate face identity processing (FIP) is a critical component of security professions. Unfortunately, however, rapid face matching as required in real-life situations such as passport controls cannot be improved via training. While in such situations accuracy is a high priority, it is neither the only, nor most important performance measure. Officers must process high-throughput information as efficiently as possible – i.e. both accurately and rapidly. In scenarios with grave public safety implications, however, efficiency is not sufficient. Suspect surveillance and mass data analysis in criminal investigations also demand processing ample sensitive material consistently over extended periods.Police agencies have sought to optimize operations through personnel selection targeting FIP abilities. Yet to date, the lab-based tests researchers have proffered neither reflect officers’ specific tasks, nor the efficiency and consistency critical to accomplishing them. Therefore, we aimed to benchmark the three most challenging FIP tests available against two work-samples — tasks developed in consultation with police practitioners to measure specific, situationally critical performance. We solicited participation from 390 police professionals from regional police and criminal investigation departments, yielding a representative sample of 114 participating Security Officers, Mass Data Analysts, and Search Unit Members who regularly employ FIP skills in their work.Data-driven analyses of officers’ FIP abilities revealed that work-sample efficiency and consistency represented most relevant dimensions of variation, and accounted for lab-test performance. Furthermore, performance on either work-sample was better predicted by performance on the other, than by lab-based test scores. This demonstrates the limitations of lab-based tests for applied settings, and stresses the need for predicting police officers’ FIP abilities through contextually and practically relevant performance measures.
